## Introduction In investment management firms, mutual fund and ETF accounting systems rely heavily on accurate, consistent static data. This includes fund specifications, portfolio classifications, benchmark indices, pricing sources, regulatory mappings, and operational parameters. The Model Context Protocol (MCP) provides an excellent framework for exposing this critical data to AI agents in a structured, standardized way. This article demonstrates how to build a robust MCP server that serves mutual fund and ETF accounting static data, enabling AI agents to process fund accounting, performance analysis, and regulatory reporting intelligently while maintaining data consistency across the organization. ## Understanding the Model Context Protocol (MCP) ### What is MCP? The Model Context Protocol (MCP) is an open standard that enables seamless integration between AI agents and data sources, tools, and services. Unlike traditional APIs that require custom integration work for each AI system, MCP provides a standardized way for AI agents to discover, access, and interact with external resources. MCP was designed specifically to address the challenges of building AI systems that need to work with diverse, dynamic data sources while maintaining consistency, security, and scalability. ### Key MCP Concepts **Resources**: Static or dynamic data that AI agents can read and reference. In our investment management context, this includes fund master data, security reference information, benchmark details, and regulatory classifications. **Tools**: Functions that AI agents can invoke to perform operations or calculations. Examples include trade validation, NAV calculation, performance attribution, and risk analytics. **Prompts**: Reusable templates that help AI agents interact with the data and tools in consistent ways. **Sampling**: The ability for AI agents to explore and understand the structure of data before making requests. ### MCP Architecture Benefits ```mermaid graph TB subgraph "AI Agents" A1[Portfolio Management Agent] A2[Risk Analytics Agent] A3[Compliance Agent] A4[Performance Agent] end subgraph "MCP Layer" MCP[MCP Server
Fund Accounting Data] end subgraph "Data Sources" DB[Fund Database] PS[Pricing Services] RS[Risk Systems] CS[Compliance Systems] end A1 --> MCP A2 --> MCP A3 --> MCP A4 --> MCP MCP --> DB MCP --> PS MCP --> RS MCP --> CS ``` ## Why Traditional APIs Are Insufficient for AI-Driven Investment Management ### Limitations of Standard REST/GraphQL APIs #### 1. **Lack of Semantic Understanding** Traditional APIs expose data through predefined endpoints but don't provide semantic context about what the data means or how it should be used. ```python # Traditional API response - lacks context { "fund_id": "FUND001", "expense_ratio": 0.0075, "benchmark_id": "BENCH001" } # MCP provides rich context and validation { "fund": { "fund_id": "FUND001", "fund_name": "Global Equity Growth Fund", "expense_ratio": 0.0075, "fund_type": "MUTUAL_FUND", "asset_class": "EQUITY", "regulatory_classification": "40_ACT", "investment_restrictions": { "max_single_security_weight": 5.0, "allowed_asset_classes": ["EQUITY"] } }, "validation_rules": ["check_concentration_limits", "verify_asset_class_compliance"], "related_resources": ["benchmark_data", "share_classes", "pricing_rules"] } ``` #### 2. **Static Schema Limitations** REST APIs typically have fixed schemas that don't adapt to the dynamic needs of AI agents. Investment management data often requires flexible access patterns. ```yaml # Traditional API - Fixed endpoints GET /api/funds/{id} GET /api/securities/{id} GET /api/benchmarks/{id} # MCP - Dynamic resource discovery resources://fund-master # Discovers all fund data resources://security-master # Finds security classifications tools://validate_fund_trade # Discovers validation capabilities tools://calculate_performance # Finds analytical tools ``` #### 3. **No Built-in Tool Discovery** Traditional APIs don't provide mechanisms for AI agents to discover what operations are available or how to use them effectively. ```python # Traditional API - AI agent must be pre-programmed def validate_trade(fund_id, security_id, quantity): # Agent needs to know exact endpoint and parameters response = requests.post("/api/validate", { "fund_id": fund_id, "security_id": security_id, "quantity": quantity }) # Agent must handle response format manually return response.json() # MCP - AI agent discovers capabilities dynamically async def validate_trade_with_mcp(fund_id, security_id, quantity): # Agent discovers available tools tools = await mcp_client.list_tools() # Agent finds the right tool with description validation_tool = find_tool(tools, "validate_fund_trade") # Agent understands parameters from schema result = await mcp_client.call_tool( validation_tool.name, { "fund_id": fund_id, "security_id": security_id, "quantity": quantity, "trade_type": "BUY" # Tool schema guides parameter inclusion } ) # Structured response with validation results return result ``` #### 4. **Insufficient Context for Decision Making** Investment management requires understanding relationships between data elements, regulatory constraints, and business rules that traditional APIs don't capture. ```python # Traditional API - Fragmented data fund_data = get_fund(fund_id) security_data = get_security(security_id) restrictions = get_restrictions(fund_id) benchmark_data = get_benchmark(fund_data['benchmark_id']) # AI agent must manually correlate and validate relationships # MCP - Contextual, relationship-aware responses validation_result = await mcp_client.call_tool("validate_fund_trade", { "fund_id": fund_id, "security_id": security_id, "quantity": 1000, "trade_type": "BUY" }) # Returns comprehensive context { "valid": True, "fund": {...}, # Complete fund context "security": {...}, # Security details with classifications "compliance_checks": { "concentration_limit": "PASS", "asset_class_match": "PASS", "regulatory_compliance": "PASS" }, "risk_impact": { "portfolio_weight_change": 0.02, "tracking_error_impact": 0.001 }, "benchmark_alignment": { "security_in_benchmark": True, "benchmark_weight": 0.015 } } ``` #### 5. **No Standard Error Handling for AI Context** Traditional APIs return HTTP status codes and error messages designed for human developers, not AI agents that need to understand and react to different types of failures. ```python # Traditional API error - Not AI-friendly { "status": 400, "error": "Bad Request", "message": "Invalid fund ID" } # MCP error handling - AI-actionable { "valid": False, "error": "Fund FUND999 not found", "error_type": "RESOURCE_NOT_FOUND", "suggestions": [ { "fund_id": "FUND001", "fund_name": "Global Equity Growth Fund", "similarity_score": 0.8 } ], "corrective_actions": [ "Verify fund ID spelling", "Check if fund has been deactivated", "Use fund search tool to find similar funds" ] } ``` ### MCP's Advantages for Investment Management AI #### 1. **Intelligent Resource Discovery** ```python # AI agent can discover what data is available resources = await mcp_client.list_resources() for resource in resources: print(f"Available: {resource.name} - {resource.description}") # Output: # Available: Fund Master - Complete fund data with specifications # Available: Security Master - Security reference with identifiers # Available: Benchmark Indices - Performance benchmarks and constituents # Available: Pricing Rules - Security pricing methodology and validation ``` #### 2. **Self-Documenting Tools** ```python # AI agent understands tool capabilities from schema tools = await mcp_client.list_tools() trade_validation_tool = find_tool(tools, "validate_fund_trade") print(trade_validation_tool.description) # "Validate if a trade complies with fund restrictions and parameters" print(trade_validation_tool.inputSchema) # Shows required parameters, types, and constraints ``` #### 3. **Contextual Data Relationships** MCP responses include related information that AI agents need for decision-making, reducing the number of API calls and providing richer context. ```python # Single MCP call provides comprehensive context result = await mcp_client.call_tool("calculate_fund_nav", { "fund_id": "FUND001", "nav_date": "2024-01-31" }) # Response includes: # - Fund specifications # - All holdings with current positions # - Pricing sources and validation status # - Expense accruals # - Share class information # - Historical NAV trends ``` #### 4. **Built-in Validation and Business Rules** MCP tools can encapsulate complex business logic that AI agents can invoke without understanding the underlying complexity. ```python # AI agent doesn't need to understand investment restrictions # MCP tool handles all validation logic compliance_check = await mcp_client.call_tool("validate_investment_compliance", { "fund_id": "FUND001", "proposed_trade": { "security_id": "SEC001", "quantity": 10000, "trade_type": "BUY" } }) # Returns comprehensive compliance analysis # - Concentration limits # - Asset class restrictions # - Regulatory requirements # - Risk limit impacts ``` ### Integration Complexity Comparison #### Traditional API Integration ```python class InvestmentDataService: def __init__(self): self.fund_api = FundAPI(base_url="https://funds.company.com/api") self.security_api = SecurityAPI(base_url="https://securities.company.com/api") self.pricing_api = PricingAPI(base_url="https://pricing.company.com/api") self.risk_api = RiskAPI(base_url="https://risk.company.com/api") async def validate_trade(self, fund_id, security_id, quantity): # Multiple API calls with manual error handling try: fund = await self.fund_api.get_fund(fund_id) security = await self.security_api.get_security(security_id) restrictions = await self.fund_api.get_restrictions(fund_id) # Manual validation logic if security.asset_class not in fund.allowed_asset_classes: return {"valid": False, "reason": "Asset class mismatch"} # More validation logic... except FundNotFoundError: return {"valid": False, "reason": "Fund not found"} except SecurityNotFoundError: return {"valid": False, "reason": "Security not found"} except Exception as e: return {"valid": False, "reason": f"Validation failed: {str(e)}"} ``` #### MCP Integration ```python class InvestmentMCPService: def __init__(self): self.mcp_client = MCPClient("fund-accounting-static-data") async def validate_trade(self, fund_id, security_id, quantity): # Single MCP tool call with comprehensive validation return await self.mcp_client.call_tool("validate_fund_trade", { "fund_id": fund_id, "security_id": security_id, "quantity": quantity, "trade_type": "BUY" }) # MCP handles all error cases, validation logic, and relationships ``` The MCP approach reduces integration complexity from hundreds of lines of code to just a few lines, while providing richer functionality and better error handling. ## Use Case: Investment Management Fund Accounting ### The Challenge Investment management firms face several challenges when managing fund accounting data: - **Data Consistency**: Multiple systems need access to the same fund reference data across portfolio management, accounting, and reporting - **Real-time Validation**: Fund transactions must be validated against current fund parameters, restrictions, and regulatory requirements - **Complex Classifications**: Securities need proper classification for risk management, regulatory reporting, and performance attribution - **Benchmark Tracking**: Accurate benchmark and index data for performance measurement and risk analytics - **Regulatory Compliance**: Proper fund categorization, risk metrics, and reporting requirements compliance - **Agent Integration**: AI agents need structured access to this data for automated fund accounting, compliance monitoring, and reporting ### The Solution: MCP Server Architecture An MCP server provides a standardized interface that multiple AI agents can use to access mutual fund and ETF accounting static data. This ensures consistency, reduces duplication, and enables sophisticated fund accounting workflows. ## Core Data Models ### Fund and Security Structure ```python from dataclasses import dataclass from typing import List, Optional, Dict from enum import Enum from decimal import Decimal from datetime import date class FundType(Enum): MUTUAL_FUND = "MUTUAL_FUND" ETF = "ETF" CLOSED_END_FUND = "CLOSED_END_FUND" HEDGE_FUND = "HEDGE_FUND" MONEY_MARKET = "MONEY_MARKET" class AssetClass(Enum): EQUITY = "EQUITY" FIXED_INCOME = "FIXED_INCOME" ALTERNATIVE = "ALTERNATIVE" CASH = "CASH" COMMODITY = "COMMODITY" REAL_ESTATE = "REAL_ESTATE" class PricingSource(Enum): BLOOMBERG = "BLOOMBERG" REUTERS = "REUTERS" FACTSET = "FACTSET" MSCI = "MSCI" VENDOR_SPECIFIC = "VENDOR_SPECIFIC" @dataclass class Fund: fund_id: str fund_name: str fund_type: FundType asset_class: AssetClass inception_date: date base_currency: str expense_ratio: Decimal management_fee: Decimal benchmark_id: str nav_frequency: str # DAILY, WEEKLY, MONTHLY is_active: bool regulatory_classification: str # 40 Act, UCITS, etc. investment_objective: str minimum_investment: Decimal share_classes: List[str] @dataclass class ShareClass: share_class_id: str fund_id: str class_name: str ticker_symbol: Optional[str] isin: Optional[str] cusip: Optional[str] expense_ratio: Decimal management_fee: Decimal distribution_fee: Decimal load_front: Decimal load_back: Decimal minimum_investment: Decimal currency: str is_active: bool @dataclass class Benchmark: benchmark_id: str benchmark_name: str provider: str asset_class: AssetClass currency: str pricing_source: PricingSource calculation_method: str is_active: bool constituent_count: Optional[int] @dataclass class SecurityMaster: security_id: str security_name: str asset_class: AssetClass security_type: str # STOCK, BOND, OPTION, FUTURE, etc. currency: str country_of_risk: str sector: Optional[str] industry: Optional[str] pricing_source: PricingSource exchange: Optional[str] is_active: bool identifiers: Dict[str, str] # ISIN, CUSIP, SEDOL, etc. @dataclass class PricingRule: rule_id: str security_id: str pricing_source: PricingSource pricing_method: str # MARKET, MODEL, BROKER, MATRIX validation_rules: List[str] fallback_sources: List[str] stale_price_threshold_hours: int is_active: bool ``` ## Python Implementation ### MCP Server Setup ```python import asyncio import json from typing import Any, Dict, List from decimal import Decimal from datetime import date, datetime from mcp.server import Server from mcp.server.stdio import stdio_server from mcp.types import ( Resource, Tool, TextContent, GetResourceRequest, CallToolRequest, ListResourcesRequest, ListToolsRequest ) class FundAccountingMCPServer: def __init__(self): self.server = Server("fund-accounting-static-data") self._setup_handlers() # Sample data - in production, this would come from database self.funds = self._load_funds() self.share_classes = self._load_share_classes() self.benchmarks = self._load_benchmarks() self.security_master = self._load_security_master() self.pricing_rules = self._load_pricing_rules() self.fund_restrictions = self._load_fund_restrictions() def _setup_handlers(self): """Set up MCP protocol handlers""" @self.server.list_resources() async def list_resources() -> List[Resource]: return [ Resource( uri="funds://fund-master", name="Fund Master", mimeType="application/json", description="Complete fund master data with specifications and parameters" ), Resource( uri="funds://share-classes", name="Share Classes", mimeType="application/json", description="Share class details with fees and minimums" ), Resource( uri="funds://benchmarks", name="Benchmark Indices", mimeType="application/json", description="Benchmark and index reference data" ), Resource( uri="funds://security-master", name="Security Master", mimeType="application/json", description="Security reference data with classifications" ), Resource( uri="funds://pricing-rules", name="Pricing Rules", mimeType="application/json", description="Security pricing methodology and validation rules" ), Resource( uri="funds://fund-restrictions", name="Fund Restrictions", mimeType="application/json", description="Investment restrictions and compliance rules by fund" ) ] @self.server.get_resource() async def get_resource(request: GetResourceRequest) -> str: if request.uri == "funds://fund-master": return self._serialize_data(self.funds) elif request.uri == "funds://share-classes": return self._serialize_data(self.share_classes) elif request.uri == "funds://benchmarks": return self._serialize_data(self.benchmarks) elif request.uri == "funds://security-master": return self._serialize_data(self.security_master) elif request.uri == "funds://pricing-rules": return self._serialize_data(self.pricing_rules) elif request.uri == "funds://fund-restrictions": return self._serialize_data(self.fund_restrictions) else: raise ValueError(f"Unknown resource: {request.uri}") @self.server.list_tools() async def list_tools() -> List[Tool]: return [ Tool( name="validate_fund_trade", description="Validate if a trade complies with fund restrictions and parameters", inputSchema={ "type": "object", "properties": { "fund_id": {"type": "string"}, "security_id": {"type": "string"}, "quantity": {"type": "number"}, "trade_type": {"type": "string", "enum": ["BUY", "SELL"]}, "trade_amount": {"type": "number"} }, "required": ["fund_id", "security_id", "quantity", "trade_type"] } ), Tool( name="get_fund_benchmark", description="Get benchmark information for a specific fund", inputSchema={ "type": "object", "properties": { "fund_id": {"type": "string"} }, "required": ["fund_id"] } ), Tool( name="lookup_security_details", description="Get detailed security information including identifiers and classifications", inputSchema={ "type": "object", "properties": { "identifier": {"type": "string"}, "identifier_type": {"type": "string", "enum": ["ISIN", "CUSIP", "SEDOL", "TICKER", "INTERNAL_ID"]} }, "required": ["identifier", "identifier_type"] } ), Tool( name="calculate_fund_metrics", description="Calculate key fund metrics like expense ratios, turnover, etc.", inputSchema={ "type": "object", "properties": { "fund_id": {"type": "string"}, "calculation_date": {"type": "string", "format": "date"}, "metrics": {"type": "array", "items": {"type": "string"}} }, "required": ["fund_id", "calculation_date"] } ), Tool( name="get_pricing_source", description="Get pricing source and methodology for a security", inputSchema={ "type": "object", "properties": { "security_id": {"type": "string"}, "pricing_date": {"type": "string", "format": "date"} }, "required": ["security_id"] } ), Tool( name="validate_fund_classification", description="Validate fund classification for regulatory reporting", inputSchema={ "type": "object", "properties": { "fund_id": {"type": "string"}, "classification_type": {"type": "string", "enum": ["GICS", "MORNINGSTAR", "REGULATORY", "CUSTOM"]} }, "required": ["fund_id", "classification_type"] } ) ] @self.server.call_tool() async def call_tool(request: CallToolRequest) -> List[TextContent]: tool_handlers = { "validate_fund_trade": self._validate_fund_trade, "get_fund_benchmark": self._get_fund_benchmark, "lookup_security_details": self._lookup_security_details, "calculate_fund_metrics": self._calculate_fund_metrics, "get_pricing_source": self._get_pricing_source, "validate_fund_classification": self._validate_fund_classification } if request.name in tool_handlers: return await tool_handlers[request.name](request.arguments) else: raise ValueError(f"Unknown tool: {request.name}") async def _validate_fund_trade(self, args: Dict[str, Any]) -> List[TextContent]: """Validate fund trade against restrictions and parameters""" fund_id = args["fund_id"] security_id = args["security_id"] quantity = args["quantity"] trade_type = args["trade_type"] trade_amount = args.get("trade_amount", 0) # Find fund fund = next((f for f in self.funds if f.fund_id == fund_id), None) if not fund: return self._create_response({ "valid": False, "error": f"Fund {fund_id} not found" }) # Find security security = next((s for s in self.security_master if s.security_id == security_id), None) if not security: return self._create_response({ "valid": False, "error": f"Security {security_id} not found" }) # Get fund restrictions restrictions = next((r for r in self.fund_restrictions if r["fund_id"] == fund_id), None) validation_result = { "valid": True, "fund": self._dataclass_to_dict(fund), "security": self._dataclass_to_dict(security), "validations": [], "warnings": [] } # Validate asset class restrictions if restrictions and "allowed_asset_classes" in restrictions: if security.asset_class.value not in restrictions["allowed_asset_classes"]: validation_result["valid"] = False validation_result["validations"].append( f"Asset class {security.asset_class.value} not allowed for fund {fund_id}" ) # Validate concentration limits if restrictions and "max_single_security_weight" in restrictions: max_weight = restrictions["max_single_security_weight"] validation_result["warnings"].append( f"Monitor concentration limit: max {max_weight}% per security" ) # Currency validation if security.currency != fund.base_currency: validation_result["warnings"].append( f"Currency mismatch: Security in {security.currency}, Fund base currency {fund.base_currency}" ) return self._create_response(validation_result) async def _get_fund_benchmark(self, args: Dict[str, Any]) -> List[TextContent]: """Get benchmark information for a fund""" fund_id = args["fund_id"] fund = next((f for f in self.funds if f.fund_id == fund_id), None) if not fund: return self._create_response({ "error": f"Fund {fund_id} not found" }) benchmark = next((b for b in self.benchmarks if b.benchmark_id == fund.benchmark_id), None) if not benchmark: return self._create_response({ "error": f"Benchmark {fund.benchmark_id} not found for fund {fund_id}" }) return self._create_response({ "fund_id": fund_id, "fund_name": fund.fund_name, "benchmark": self._dataclass_to_dict(benchmark) }) async def _lookup_security_details(self, args: Dict[str, Any]) -> List[TextContent]: """Lookup security by identifier""" identifier = args["identifier"] identifier_type = args["identifier_type"] security = None if identifier_type == "INTERNAL_ID": security = next((s for s in self.security_master if s.security_id == identifier), None) else: security = next((s for s in self.security_master if identifier in s.identifiers.values()), None) if not security: return self._create_response({ "found": False, "message": f"Security not found for {identifier_type}: {identifier}" }) # Get pricing rules for this security pricing_rule = next((pr for pr in self.pricing_rules if pr.security_id == security.security_id), None) result = { "found": True, "security": self._dataclass_to_dict(security), "pricing_rule": self._dataclass_to_dict(pricing_rule) if pricing_rule else None } return self._create_response(result) async def _calculate_fund_metrics(self, args: Dict[str, Any]) -> List[TextContent]: """Calculate fund metrics""" fund_id = args["fund_id"] calculation_date = args["calculation_date"] requested_metrics = args.get("metrics", ["expense_ratio", "management_fee"]) fund = next((f for f in self.funds if f.fund_id == fund_id), None) if not fund: return self._create_response({ "error": f"Fund {fund_id} not found" }) metrics = {} if "expense_ratio" in requested_metrics: metrics["expense_ratio"] = float(fund.expense_ratio) if "management_fee" in requested_metrics: metrics["management_fee"] = float(fund.management_fee) if "total_net_assets" in requested_metrics: # In production, this would be calculated from positions metrics["total_net_assets"] = 1000000.0 # Sample value if "number_of_holdings" in requested_metrics: # In production, this would be calculated from positions metrics["number_of_holdings"] = 150 # Sample value return self._create_response({ "fund_id": fund_id, "calculation_date": calculation_date, "metrics": metrics }) def _serialize_data(self, data): """Serialize dataclass objects to JSON""" if isinstance(data, list): return json.dumps([self._dataclass_to_dict(item) for item in data], indent=2, default=str) else: return json.dumps(self._dataclass_to_dict(data), indent=2, default=str) def _dataclass_to_dict(self, obj): """Convert dataclass to dictionary""" if hasattr(obj, '__dict__'): result = {} for key, value in obj.__dict__.items(): if isinstance(value, Enum): result[key] = value.value elif isinstance(value, (date, datetime)): result[key] = value.isoformat() elif isinstance(value, Decimal): result[key] = float(value) else: result[key] = value return result return obj def _create_response(self, data): """Create standardized response""" return [TextContent( type="text", text=json.dumps(data, indent=2, default=str) )] def _load_funds(self) -> List[Fund]: """Load fund master data""" return [ Fund( fund_id="FUND001", fund_name="Global Equity Growth Fund", fund_type=FundType.MUTUAL_FUND, asset_class=AssetClass.EQUITY, inception_date=date(2010, 1, 15), base_currency="USD", expense_ratio=Decimal("0.0075"), management_fee=Decimal("0.0050"), benchmark_id="BENCH001", nav_frequency="DAILY", is_active=True, regulatory_classification="40_ACT", investment_objective="Long-term capital growth through global equity investments", minimum_investment=Decimal("10000"), share_classes=["A", "I", "C"] ), Fund( fund_id="ETF001", fund_name="Technology Sector ETF", fund_type=FundType.ETF, asset_class=AssetClass.EQUITY, inception_date=date(2015, 6, 1), base_currency="USD", expense_ratio=Decimal("0.0025"), management_fee=Decimal("0.0020"), benchmark_id="BENCH002", nav_frequency="DAILY", is_active=True, regulatory_classification="40_ACT", investment_objective="Track the performance of technology sector stocks", minimum_investment=Decimal("1"), share_classes=["ETF"] ), Fund( fund_id="FUND002", fund_name="International Bond Fund", fund_type=FundType.MUTUAL_FUND, asset_class=AssetClass.FIXED_INCOME, inception_date=date(2012, 3, 20), base_currency="USD", expense_ratio=Decimal("0.0060"), management_fee=Decimal("0.0040"), benchmark_id="BENCH003", nav_frequency="DAILY", is_active=True, regulatory_classification="40_ACT", investment_objective="Income generation through international fixed income securities", minimum_investment=Decimal("5000"), share_classes=["A", "I"] ) ] def _load_share_classes(self) -> List[ShareClass]: """Load share class data""" return [ ShareClass( share_class_id="FUND001_A", fund_id="FUND001", class_name="Class A", ticker_symbol="GEGRX", isin="US12345678901", cusip="123456789", expense_ratio=Decimal("0.0100"), management_fee=Decimal("0.0050"), distribution_fee=Decimal("0.0025"), load_front=Decimal("0.0575"), load_back=Decimal("0.0000"), minimum_investment=Decimal("10000"), currency="USD", is_active=True ), ShareClass( share_class_id="FUND001_I", fund_id="FUND001", class_name="Institutional", ticker_symbol="GEGIX", isin="US12345678902", cusip="123456790", expense_ratio=Decimal("0.0075"), management_fee=Decimal("0.0050"), distribution_fee=Decimal("0.0000"), load_front=Decimal("0.0000"), load_back=Decimal("0.0000"), minimum_investment=Decimal("1000000"), currency="USD", is_active=True ), ShareClass( share_class_id="ETF001_ETF", fund_id="ETF001", class_name="ETF Shares", ticker_symbol="TECH", isin="US12345678903", cusip="123456791", expense_ratio=Decimal("0.0025"), management_fee=Decimal("0.0020"), distribution_fee=Decimal("0.0000"), load_front=Decimal("0.0000"), load_back=Decimal("0.0000"), minimum_investment=Decimal("1"), currency="USD", is_active=True ) ] def _load_benchmarks(self) -> List[Benchmark]: """Load benchmark data""" return [ Benchmark( benchmark_id="BENCH001", benchmark_name="MSCI World Index", provider="MSCI", asset_class=AssetClass.EQUITY, currency="USD", pricing_source=PricingSource.MSCI, calculation_method="MARKET_CAP_WEIGHTED", is_active=True, constituent_count=1500 ), Benchmark( benchmark_id="BENCH002", benchmark_name="NASDAQ Technology Index", provider="NASDAQ", asset_class=AssetClass.EQUITY, currency="USD", pricing_source=PricingSource.BLOOMBERG, calculation_method="MARKET_CAP_WEIGHTED", is_active=True, constituent_count=100 ), Benchmark( benchmark_id="BENCH003", benchmark_name="Bloomberg Global Aggregate Bond Index", provider="Bloomberg", asset_class=AssetClass.FIXED_INCOME, currency="USD", pricing_source=PricingSource.BLOOMBERG, calculation_method="MARKET_VALUE_WEIGHTED", is_active=True, constituent_count=25000 ) ] def _load_security_master(self) -> List[SecurityMaster]: """Load security master data""" return [ SecurityMaster( security_id="SEC001", security_name="Apple Inc.", asset_class=AssetClass.EQUITY, security_type="COMMON_STOCK", currency="USD", country_of_risk="US", sector="Technology", industry="Technology Hardware", pricing_source=PricingSource.BLOOMBERG, exchange="NASDAQ", is_active=True, identifiers={ "ISIN": "US0378331005", "CUSIP": "037833100", "TICKER": "AAPL", "SEDOL": "2046251" } ), SecurityMaster( security_id="SEC002", security_name="US Treasury 2.5% 2030", asset_class=AssetClass.FIXED_INCOME, security_type="GOVERNMENT_BOND", currency="USD", country_of_risk="US", sector="Government", industry="Treasury", pricing_source=PricingSource.BLOOMBERG, exchange=None, is_active=True, identifiers={ "ISIN": "US912828XG55", "CUSIP": "912828XG5", "TICKER": "T 2.5 08/15/30" } ), SecurityMaster( security_id="SEC003", security_name="Microsoft Corporation", asset_class=AssetClass.EQUITY, security_type="COMMON_STOCK", currency="USD", country_of_risk="US", sector="Technology", industry="Software", pricing_source=PricingSource.BLOOMBERG, exchange="NASDAQ", is_active=True, identifiers={ "ISIN": "US5949181045", "CUSIP": "594918104", "TICKER": "MSFT", "SEDOL": "2588173" } ) ] def _load_pricing_rules(self) -> List[PricingRule]: """Load pricing rules""" return [ PricingRule( rule_id="RULE001", security_id="SEC001", pricing_source=PricingSource.BLOOMBERG, pricing_method="MARKET", validation_rules=["STALE_PRICE_CHECK", "PRICE_VARIANCE_CHECK"], fallback_sources=["REUTERS", "FACTSET"], stale_price_threshold_hours=24, is_active=True ), PricingRule( rule_id="RULE002", security_id="SEC002", pricing_source=PricingSource.BLOOMBERG, pricing_method="MATRIX", validation_rules=["YIELD_CURVE_CHECK", "CREDIT_SPREAD_CHECK"], fallback_sources=["REUTERS"], stale_price_threshold_hours=24, is_active=True ), PricingRule( rule_id="RULE003", security_id="SEC003", pricing_source=PricingSource.BLOOMBERG, pricing_method="MARKET", validation_rules=["STALE_PRICE_CHECK", "VOLUME_CHECK"], fallback_sources=["REUTERS", "FACTSET"], stale_price_threshold_hours=24, is_active=True ) ] def _load_fund_restrictions(self) -> List[Dict]: """Load fund investment restrictions""" return [ { "fund_id": "FUND001", "allowed_asset_classes": ["EQUITY"], "max_single_security_weight": 5.0, "max_sector_weight": 25.0, "max_country_weight": 30.0, "min_holdings": 50, "max_holdings": 200, "liquidity_requirements": { "min_daily_liquidity_pct": 10.0, "max_illiquid_securities_pct": 15.0 } }, { "fund_id": "ETF001", "allowed_asset_classes": ["EQUITY"], "max_single_security_weight": 25.0, "max_sector_weight": 100.0, "sector_focus": "Technology", "tracking_error_limit": 0.50, "replication_method": "FULL" }, { "fund_id": "FUND002", "allowed_asset_classes": ["FIXED_INCOME"], "max_single_security_weight": 5.0, "min_credit_quality": "BBB", "max_duration": 10.0, "currency_hedging": True, "geographic_focus": "INTERNATIONAL_EX_US" } ] # Server startup async def main(): server_instance = FundAccountingMCPServer() async with stdio_server() as (read_stream, write_stream): await server_instance.server.run( read_stream, write_stream, server_instance.server.create_initialization_options() ) if __name__ == "__main__": asyncio.run(main()) ``` ## Java Implementation ### MCP Server in Java ```java package com.investment.fund.mcp; import com.fasterxml.jackson.databind.ObjectMapper; import com.fasterxml.jackson.databind.node.ArrayNode; import com.fasterxml.jackson.databind.node.ObjectNode; import java.math.BigDecimal; import java.time.LocalDate; import java.util.*; import java.util.stream.Collectors; public class FundAccountingMCPServer { private final ObjectMapper objectMapper = new ObjectMapper(); private final List funds; private final List shareClasses; private final List benchmarks; private final List securityMaster; private final List pricingRules; public FundAccountingMCPServer() { this.funds = loadFunds(); this.shareClasses = loadShareClasses(); this.benchmarks = loadBenchmarks(); this.securityMaster = loadSecurityMaster(); this.pricingRules = loadPricingRules(); } // Data Models public static class Fund { public String fundId; public String fundName; public FundType fundType; public AssetClass assetClass; public LocalDate inceptionDate; public String baseCurrency; public BigDecimal expenseRatio; public BigDecimal managementFee; public String benchmarkId; public String navFrequency; public boolean isActive; public String regulatoryClassification; public String investmentObjective; public BigDecimal minimumInvestment; public List shareClasses; public Fund(String fundId, String fundName, FundType fundType, AssetClass assetClass, LocalDate inceptionDate, String baseCurrency, BigDecimal expenseRatio, BigDecimal managementFee, String benchmarkId, String navFrequency, boolean isActive, String regulatoryClassification, String investmentObjective, BigDecimal minimumInvestment, List shareClasses) { this.fundId = fundId; this.fundName = fundName; this.fundType = fundType; this.assetClass = assetClass; this.inceptionDate = inceptionDate; this.baseCurrency = baseCurrency; this.expenseRatio = expenseRatio; this.managementFee = managementFee; this.benchmarkId = benchmarkId; this.navFrequency = navFrequency; this.isActive = isActive; this.regulatoryClassification = regulatoryClassification; this.investmentObjective = investmentObjective; this.minimumInvestment = minimumInvestment; this.shareClasses = shareClasses; } } public static class ShareClass { public String shareClassId; public String fundId; public String className; public String tickerSymbol; public String isin; public String cusip; public BigDecimal expenseRatio; public BigDecimal managementFee; public BigDecimal distributionFee; public BigDecimal loadFront; public BigDecimal loadBack; public BigDecimal minimumInvestment; public String currency; public boolean isActive; public ShareClass(String shareClassId, String fundId, String className, String tickerSymbol, String isin, String cusip, BigDecimal expenseRatio, BigDecimal managementFee, BigDecimal distributionFee, BigDecimal loadFront, BigDecimal loadBack, BigDecimal minimumInvestment, String currency, boolean isActive) { this.shareClassId = shareClassId; this.fundId = fundId; this.className = className; this.tickerSymbol = tickerSymbol; this.isin = isin; this.cusip = cusip; this.expenseRatio = expenseRatio; this.managementFee = managementFee; this.distributionFee = distributionFee; this.loadFront = loadFront; this.loadBack = loadBack; this.minimumInvestment = minimumInvestment; this.currency = currency; this.isActive = isActive; } } public static class Benchmark { public String benchmarkId; public String benchmarkName; public String provider; public AssetClass assetClass; public String currency; public PricingSource pricingSource; public String calculationMethod; public boolean isActive; public Integer constituentCount; public Benchmark(String benchmarkId, String benchmarkName, String provider, AssetClass assetClass, String currency, PricingSource pricingSource, String calculationMethod, boolean isActive, Integer constituentCount) { this.benchmarkId = benchmarkId; this.benchmarkName = benchmarkName; this.provider = provider; this.assetClass = assetClass; this.currency = currency; this.pricingSource = pricingSource; this.calculationMethod = calculationMethod; this.isActive = isActive; this.constituentCount = constituentCount; } } public static class SecurityMaster { public String securityId; public String securityName; public AssetClass assetClass; public String securityType; public String currency; public String countryOfRisk; public String sector; public String industry; public PricingSource pricingSource; public String exchange; public boolean isActive; public Map identifiers; public SecurityMaster(String securityId, String securityName, AssetClass assetClass, String securityType, String currency, String countryOfRisk, String sector, String industry, PricingSource pricingSource, String exchange, boolean isActive, Map identifiers) { this.securityId = securityId; this.securityName = securityName; this.assetClass = assetClass; this.securityType = securityType; this.currency = currency; this.countryOfRisk = countryOfRisk; this.sector = sector; this.industry = industry; this.pricingSource = pricingSource; this.exchange = exchange; this.isActive = isActive; this.identifiers = identifiers; } } public static class PricingRule { public String ruleId; public String securityId; public PricingSource pricingSource; public String pricingMethod; public List validationRules; public List fallbackSources; public int stalePriceThresholdHours; public boolean isActive; public PricingRule(String ruleId, String securityId, PricingSource pricingSource, String pricingMethod, List validationRules, List fallbackSources, int stalePriceThresholdHours, boolean isActive) { this.ruleId = ruleId; this.securityId = securityId; this.pricingSource = pricingSource; this.pricingMethod = pricingMethod; this.validationRules = validationRules; this.fallbackSources = fallbackSources; this.stalePriceThresholdHours = stalePriceThresholdHours; this.isActive = isActive; } } public enum FundType { MUTUAL_FUND, ETF, CLOSED_END_FUND, HEDGE_FUND, MONEY_MARKET } public enum AssetClass { EQUITY, FIXED_INCOME, ALTERNATIVE, CASH, COMMODITY, REAL_ESTATE } public enum PricingSource { BLOOMBERG, REUTERS, FACTSET, MSCI, VENDOR_SPECIFIC } // MCP Protocol Handlers public Object handleListResources() { ObjectNode response = objectMapper.createObjectNode(); ArrayNode resources = objectMapper.createArrayNode(); resources.add(createResource( "funds://fund-master", "Fund Master", "application/json", "Complete fund master data with specifications and parameters" )); resources.add(createResource( "funds://share-classes", "Share Classes", "application/json", "Share class details with fees and minimums" )); resources.add(createResource( "funds://benchmarks", "Benchmark Indices", "application/json", "Benchmark and index reference data" )); resources.add(createResource( "funds://security-master", "Security Master", "application/json", "Security reference data with classifications" )); resources.add(createResource( "funds://pricing-rules", "Pricing Rules", "application/json", "Security pricing methodology and validation rules" )); response.set("resources", resources); return response; } public Object handleGetResource(String uri) throws Exception { switch (uri) { case "funds://fund-master": return objectMapper.writeValueAsString(funds); case "funds://share-classes": return objectMapper.writeValueAsString(shareClasses); case "funds://benchmarks": return objectMapper.writeValueAsString(benchmarks); case "funds://security-master": return objectMapper.writeValueAsString(securityMaster); case "funds://pricing-rules": return objectMapper.writeValueAsString(pricingRules); default: throw new IllegalArgumentException("Unknown resource: " + uri); } } public Object handleListTools() { ObjectNode response = objectMapper.createObjectNode(); ArrayNode tools = objectMapper.createArrayNode(); // Validate Fund Trade Tool ObjectNode validateTradeTool = objectMapper.createObjectNode(); validateTradeTool.put("name", "validate_fund_trade"); validateTradeTool.put("description", "Validate if a trade complies with fund restrictions and parameters"); ObjectNode validateTradeSchema = objectMapper.createObjectNode(); validateTradeSchema.put("type", "object"); ObjectNode validateTradeProps = objectMapper.createObjectNode(); validateTradeProps.set("fund_id", objectMapper.createObjectNode().put("type", "string")); validateTradeProps.set("security_id", objectMapper.createObjectNode().put("type", "string")); validateTradeProps.set("quantity", objectMapper.createObjectNode().put("type", "number")); ObjectNode tradeTypeEnum = objectMapper.createObjectNode(); tradeTypeEnum.put("type", "string"); ArrayNode tradeTypeOptions = objectMapper.createArrayNode(); tradeTypeOptions.add("BUY"); tradeTypeOptions.add("SELL"); tradeTypeEnum.set("enum", tradeTypeOptions); validateTradeProps.set("trade_type", tradeTypeEnum); validateTradeProps.set("trade_amount", objectMapper.createObjectNode().put("type", "number")); validateTradeSchema.set("properties", validateTradeProps); ArrayNode validateTradeRequired = objectMapper.createArrayNode(); validateTradeRequired.add("fund_id"); validateTradeRequired.add("security_id"); validateTradeRequired.add("quantity"); validateTradeRequired.add("trade_type"); validateTradeSchema.set("required", validateTradeRequired); validateTradeTool.set("inputSchema", validateTradeSchema); tools.add(validateTradeTool); // Get Fund Benchmark Tool ObjectNode benchmarkTool = objectMapper.createObjectNode(); benchmarkTool.put("name", "get_fund_benchmark"); benchmarkTool.put("description", "Get benchmark information for a specific fund"); ObjectNode benchmarkSchema = objectMapper.createObjectNode(); benchmarkSchema.put("type", "object"); ObjectNode benchmarkProps = objectMapper.createObjectNode(); benchmarkProps.set("fund_id", objectMapper.createObjectNode().put("type", "string")); benchmarkSchema.set("properties", benchmarkProps); ArrayNode benchmarkRequired = objectMapper.createArrayNode(); benchmarkRequired.add("fund_id"); benchmarkSchema.set("required", benchmarkRequired); benchmarkTool.set("inputSchema", benchmarkSchema); tools.add(benchmarkTool); // Lookup Security Details Tool ObjectNode securityTool = objectMapper.createObjectNode(); securityTool.put("name", "lookup_security_details"); securityTool.put("description", "Get detailed security information including identifiers and classifications"); ObjectNode securitySchema = objectMapper.createObjectNode(); securitySchema.put("type", "object"); ObjectNode securityProps = objectMapper.createObjectNode(); securityProps.set("identifier", objectMapper.createObjectNode().put("type", "string")); ObjectNode identifierTypeEnum = objectMapper.createObjectNode(); identifierTypeEnum.put("type", "string"); ArrayNode identifierTypeOptions = objectMapper.createArrayNode(); identifierTypeOptions.add("ISIN"); identifierTypeOptions.add("CUSIP"); identifierTypeOptions.add("SEDOL"); identifierTypeOptions.add("TICKER"); identifierTypeOptions.add("INTERNAL_ID"); identifierTypeEnum.set("enum", identifierTypeOptions); securityProps.set("identifier_type", identifierTypeEnum); securitySchema.set("properties", securityProps); ArrayNode securityRequired = objectMapper.createArrayNode(); securityRequired.add("identifier"); securityRequired.add("identifier_type"); securitySchema.set("required", securityRequired); securityTool.set("inputSchema", securitySchema); tools.add(securityTool); response.set("tools", tools); return response; } public Object handleCallTool(String toolName, Map arguments) throws Exception { switch (toolName) { case "validate_fund_trade": return validateFundTrade(arguments); case "get_fund_benchmark": return getFundBenchmark(arguments); case "lookup_security_details": return lookupSecurityDetails(arguments); default: throw new IllegalArgumentException("Unknown tool: " + toolName); } } // Tool Implementations private Object validateFundTrade(Map args) { String fundId = (String) args.get("fund_id"); String securityId = (String) args.get("security_id"); Double quantity = (Double) args.get("quantity"); String tradeType = (String) args.get("trade_type"); ObjectNode result = objectMapper.createObjectNode(); // Find fund Fund fund = funds.stream() .filter(f -> f.fundId.equals(fundId)) .findFirst() .orElse(null); if (fund == null) { result.put("valid", false); result.put("error", "Fund " + fundId + " not found"); return result; } // Find security SecurityMaster security = securityMaster.stream() .filter(s -> s.securityId.equals(securityId)) .findFirst() .orElse(null); if (security == null) { result.put("valid", false); result.put("error", "Security " + securityId + " not found"); return result; } result.put("valid", true); result.set("fund", objectMapper.valueToTree(fund)); result.set("security", objectMapper.valueToTree(security)); ArrayNode validations = objectMapper.createArrayNode(); ArrayNode warnings = objectMapper.createArrayNode(); // Asset class validation if (!security.assetClass.equals(fund.assetClass)) { warnings.add("Asset class mismatch: Security is " + security.assetClass + ", Fund focuses on " + fund.assetClass); } // Currency validation if (!security.currency.equals(fund.baseCurrency)) { warnings.add("Currency mismatch: Security in " + security.currency + ", Fund base currency " + fund.baseCurrency); } result.set("validations", validations); result.set("warnings", warnings); return result; } private Object getFundBenchmark(Map args) { String fundId = (String) args.get("fund_id"); Fund fund = funds.stream() .filter(f -> f.fundId.equals(fundId)) .findFirst() .orElse(null); if (fund == null) { ObjectNode error = objectMapper.createObjectNode(); error.put("error", "Fund " + fundId + " not found"); return error; } Benchmark benchmark = benchmarks.stream() .filter(b -> b.benchmarkId.equals(fund.benchmarkId)) .findFirst() .orElse(null); if (benchmark == null) { ObjectNode error = objectMapper.createObjectNode(); error.put("error", "Benchmark " + fund.benchmarkId + " not found for fund " + fundId); return error; } ObjectNode result = objectMapper.createObjectNode(); result.put("fund_id", fundId); result.put("fund_name", fund.fundName); result.set("benchmark", objectMapper.valueToTree(benchmark)); return result; } private Object lookupSecurityDetails(Map args) { String identifier = (String) args.get("identifier"); String identifierType = (String) args.get("identifier_type"); SecurityMaster security = null; if ("INTERNAL_ID".equals(identifierType)) { security = securityMaster.stream() .filter(s -> s.securityId.equals(identifier)) .findFirst() .orElse(null); } else { security = securityMaster.stream() .filter(s -> s.identifiers.containsValue(identifier)) .findFirst() .orElse(null); } ObjectNode result = objectMapper.createObjectNode(); if (security == null) { result.put("found", false); result.put("message", "Security not found for " + identifierType + ": " + identifier); return result; } // Find pricing rule PricingRule pricingRule = pricingRules.stream() .filter(pr -> pr.securityId.equals(security.securityId)) .findFirst() .orElse(null); result.put("found", true); result.set("security", objectMapper.valueToTree(security)); if (pricingRule != null) { result.set("pricing_rule", objectMapper.valueToTree(pricingRule)); } return result; } // Helper Methods private ObjectNode createResource(String uri, String name, String mimeType, String description) { ObjectNode resource = objectMapper.createObjectNode(); resource.put("uri", uri); resource.put("name", name); resource.put("mimeType", mimeType); resource.put("description", description); return resource; } // Data Loading Methods private List loadFunds() { return Arrays.asList( new Fund("FUND001", "Global Equity Growth Fund", FundType.MUTUAL_FUND, AssetClass.EQUITY, LocalDate.of(2010, 1, 15), "USD", new BigDecimal("0.0075"), new BigDecimal("0.0050"), "BENCH001", "DAILY", true, "40_ACT", "Long-term capital growth through global equity investments", new BigDecimal("10000"), Arrays.asList("A", "I", "C")), new Fund("ETF001", "Technology Sector ETF", FundType.ETF, AssetClass.EQUITY, LocalDate.of(2015, 6, 1), "USD", new BigDecimal("0.0025"), new BigDecimal("0.0020"), "BENCH002", "DAILY", true, "40_ACT", "Track the performance of technology sector stocks", new BigDecimal("1"), Arrays.asList("ETF")), new Fund("FUND002", "International Bond Fund", FundType.MUTUAL_FUND, AssetClass.FIXED_INCOME, LocalDate.of(2012, 3, 20), "USD", new BigDecimal("0.0060"), new BigDecimal("0.0040"), "BENCH003", "DAILY", true, "40_ACT", "Income generation through international fixed income securities", new BigDecimal("5000"), Arrays.asList("A", "I")) ); } private List loadShareClasses() { return Arrays.asList( new ShareClass("FUND001_A", "FUND001", "Class A", "GEGRX", "US12345678901", "123456789", new BigDecimal("0.0100"), new BigDecimal("0.0050"), new BigDecimal("0.0025"), new BigDecimal("0.0575"), new BigDecimal("0.0000"), new BigDecimal("10000"), "USD", true), new ShareClass("FUND001_I", "FUND001", "Institutional", "GEGIX", "US12345678902", "123456790", new BigDecimal("0.0075"), new BigDecimal("0.0050"), new BigDecimal("0.0000"), new BigDecimal("0.0000"), new BigDecimal("0.0000"), new BigDecimal("1000000"), "USD", true), new ShareClass("ETF001_ETF", "ETF001", "ETF Shares", "TECH", "US12345678903", "123456791", new BigDecimal("0.0025"), new BigDecimal("0.0020"), new BigDecimal("0.0000"), new BigDecimal("0.0000"), new BigDecimal("0.0000"), new BigDecimal("1"), "USD", true) ); } private List loadBenchmarks() { return Arrays.asList( new Benchmark("BENCH001", "MSCI World Index", "MSCI", AssetClass.EQUITY, "USD", PricingSource.MSCI, "MARKET_CAP_WEIGHTED", true, 1500), new Benchmark("BENCH002", "NASDAQ Technology Index", "NASDAQ", AssetClass.EQUITY, "USD", PricingSource.BLOOMBERG, "MARKET_CAP_WEIGHTED", true, 100), new Benchmark("BENCH003", "Bloomberg Global Aggregate Bond Index", "Bloomberg", AssetClass.FIXED_INCOME, "USD", PricingSource.BLOOMBERG, "MARKET_VALUE_WEIGHTED", true, 25000) ); } private List loadSecurityMaster() { return Arrays.asList( new SecurityMaster("SEC001", "Apple Inc.", AssetClass.EQUITY, "COMMON_STOCK", "USD", "US", "Technology", "Technology Hardware", PricingSource.BLOOMBERG, "NASDAQ", true, Map.of("ISIN", "US0378331005", "CUSIP", "037833100", "TICKER", "AAPL", "SEDOL", "2046251")), new SecurityMaster("SEC002", "US Treasury 2.5% 2030", AssetClass.FIXED_INCOME, "GOVERNMENT_BOND", "USD", "US", "Government", "Treasury", PricingSource.BLOOMBERG, null, true, Map.of("ISIN", "US912828XG55", "CUSIP", "912828XG5", "TICKER", "T 2.5 08/15/30")), new SecurityMaster("SEC003", "Microsoft Corporation", AssetClass.EQUITY, "COMMON_STOCK", "USD", "US", "Technology", "Software", PricingSource.BLOOMBERG, "NASDAQ", true, Map.of("ISIN", "US5949181045", "CUSIP", "594918104", "TICKER", "MSFT", "SEDOL", "2588173")) ); } private List loadPricingRules() { return Arrays.asList( new PricingRule("RULE001", "SEC001", PricingSource.BLOOMBERG, "MARKET", Arrays.asList("STALE_PRICE_CHECK", "PRICE_VARIANCE_CHECK"), Arrays.asList("REUTERS", "FACTSET"), 24, true), new PricingRule("RULE002", "SEC002", PricingSource.BLOOMBERG, "MATRIX", Arrays.asList("YIELD_CURVE_CHECK", "CREDIT_SPREAD_CHECK"), Arrays.asList("REUTERS"), 24, true), new PricingRule("RULE003", "SEC003", PricingSource.BLOOMBERG, "MARKET", Arrays.asList("STALE_PRICE_CHECK", "VOLUME_CHECK"), Arrays.asList("REUTERS", "FACTSET"), 24, true) ); } } ``` ## Usage Examples for AI Agents ### Fund Accounting Agent ```python # Example of how an AI agent would use the MCP server for fund accounting import mcp_client async def process_fund_trade(trade_data): mcp_client = MCPClient("fund-accounting-static-data") # Validate fund trade validation_result = await mcp_client.call_tool( "validate_fund_trade", { "fund_id": trade_data["fund_id"], "security_id": trade_data["security_id"], "quantity": trade_data["quantity"], "trade_type": trade_data["trade_type"], "trade_amount": trade_data["trade_amount"] } ) if not validation_result["valid"]: return { "status": "error", "message": validation_result["error"] } # Get benchmark for performance attribution benchmark_result = await mcp_client.call_tool( "get_fund_benchmark", {"fund_id": trade_data["fund_id"]} ) # Get detailed security information security_details = await mcp_client.call_tool( "lookup_security_details", { "identifier": trade_data["security_id"], "identifier_type": "INTERNAL_ID" } ) return { "status": "validated", "fund_info": validation_result["fund"], "security_info": security_details["security"], "benchmark": benchmark_result["benchmark"], "warnings": validation_result.get("warnings", []), "pricing_rule": security_details.get("pricing_rule") } async def calculate_fund_performance(fund_id, start_date, end_date): mcp_client = MCPClient("fund-accounting-static-data") # Get fund benchmark benchmark_result = await mcp_client.call_tool( "get_fund_benchmark", {"fund_id": fund_id} ) # Calculate fund metrics metrics_result = await mcp_client.call_tool( "calculate_fund_metrics", { "fund_id": fund_id, "calculation_date": end_date, "metrics": ["expense_ratio", "total_net_assets", "number_of_holdings"] } ) return { "fund_id": fund_id, "period": f"{start_date} to {end_date}", "benchmark": benchmark_result["benchmark"], "fund_metrics": metrics_result["metrics"] } ``` ## Production Considerations ### Database Integration for Investment Data ```python class ProductionFundAccountingMCPServer(FundAccountingMCPServer): def __init__(self, db_connection): self.db = db_connection super().__init__() def _load_funds(self): """Load from investment management database""" query = """ SELECT fund_id, fund_name, fund_type, asset_class, inception_date, base_currency, expense_ratio, management_fee, benchmark_id, nav_frequency, is_active, regulatory_classification, investment_objective, minimum_investment FROM fund_master WHERE is_active = true ORDER BY fund_id """ funds = [] for row in self.db.execute(query): # Load share classes for this fund share_classes = self._load_fund_share_classes(row['fund_id']) funds.append(Fund( fund_id=row['fund_id'], fund_name=row['fund_name'], fund_type=FundType(row['fund_type']), asset_class=AssetClass(row['asset_class']), inception_date=row['inception_date'], base_currency=row['base_currency'], expense_ratio=row['expense_ratio'], management_fee=row['management_fee'], benchmark_id=row['benchmark_id'], nav_frequency=row['nav_frequency'], is_active=row['is_active'], regulatory_classification=row['regulatory_classification'], investment_objective=row['investment_objective'], minimum_investment=row['minimum_investment'], share_classes=[sc.class_name for sc in share_classes] )) return funds async def _validate_fund_trade(self, args): """Enhanced validation with real-time portfolio data""" validation_result = await super()._validate_fund_trade(args) if validation_result["valid"]: # Check current portfolio position current_position = await self._get_current_position( args["fund_id"], args["security_id"] ) # Check fund restrictions in real-time restriction_check = await self._check_investment_restrictions( args["fund_id"], args["security_id"], args["quantity"] ) validation_result["current_position"] = current_position validation_result["restriction_compliance"] = restriction_check return validation_result async def _get_current_position(self, fund_id, security_id): """Get current portfolio position""" query = """ SELECT quantity, market_value, weight_percent FROM portfolio_positions WHERE fund_id = %s AND security_id = %s AND position_date = CURRENT_DATE """ result = await self.db.fetch_one(query, fund_id, security_id) return result if result else {"quantity": 0, "market_value": 0, "weight_percent": 0} ``` ### Risk Management Integration ```python class RiskAwareFundAccountingMCPServer(ProductionFundAccountingMCPServer): def __init__(self, db_connection, risk_service): self.risk_service = risk_service super().__init__(db_connection) async def _validate_fund_trade(self, args): """Enhanced validation with risk analytics""" validation_result = await super()._validate_fund_trade(args) if validation_result["valid"]: # Calculate risk metrics impact risk_impact = await self._calculate_risk_impact( args["fund_id"], args["security_id"], args["quantity"], args["trade_type"] ) # Check VaR limits var_check = await self._check_var_limits(args["fund_id"], risk_impact) # Check tracking error tracking_error_check = await self._check_tracking_error( args["fund_id"], risk_impact ) validation_result["risk_impact"] = risk_impact validation_result["var_compliance"] = var_check validation_result["tracking_error_compliance"] = tracking_error_check return validation_result async def _calculate_risk_impact(self, fund_id, security_id, quantity, trade_type): """Calculate risk impact of proposed trade""" return await self.risk_service.calculate_trade_impact( fund_id=fund_id, security_id=security_id, quantity=quantity, trade_type=trade_type, metrics=["VAR_1D", "VAR_10D", "TRACKING_ERROR", "BETA"] ) ``` ### Security and Compliance ```python class ComplianceFundAccountingMCPServer(RiskAwareFundAccountingMCPServer): def __init__(self, db_connection, risk_service, compliance_service): self.compliance_service = compliance_service super().__init__(db_connection, risk_service) async def authenticate_request(self, request_context): """Validate API keys and fund access permissions""" api_key = request_context.get("api_key") if not self.compliance_service.validate_api_key(api_key): raise PermissionError("Invalid API key") permissions = self.compliance_service.get_permissions(api_key) return permissions async def authorize_fund_access(self, fund_id, permissions): """Check if client has access to specific fund""" allowed_funds = permissions.get("allowed_funds", []) if fund_id not in allowed_funds and "*" not in allowed_funds: raise PermissionError(f"Access denied to fund {fund_id}") async def _validate_fund_trade(self, args): """Enhanced validation with regulatory compliance""" validation_result = await super()._validate_fund_trade(args) if validation_result["valid"]: # Check regulatory limits (diversification, concentration, etc.) regulatory_check = await self._check_regulatory_compliance( args["fund_id"], args["security_id"], args["quantity"] ) # Check investment restrictions (prohibited securities, etc.) restriction_check = await self._check_investment_restrictions( args["fund_id"], args["security_id"] ) validation_result["regulatory_compliance"] = regulatory_check validation_result["investment_restrictions"] = restriction_check return validation_result ``` ## Advanced Features ### Real-time NAV Calculation ```python from decimal import Decimal import asyncio from typing import Dict, List class NAVCalculationMCPServer(ComplianceFundAccountingMCPServer): def __init__(self, db_connection, risk_service, compliance_service, pricing_service): self.pricing_service = pricing_service super().__init__(db_connection, risk_service, compliance_service) # Add NAV calculation tools self._setup_nav_tools() def _setup_nav_tools(self): """Add NAV-specific tools""" @self.server.list_tools() async def enhanced_list_tools(): base_tools = await super().list_tools() nav_tools = [ Tool( name="calculate_fund_nav", description="Calculate Net Asset Value for a fund", inputSchema={ "type": "object", "properties": { "fund_id": {"type": "string"}, "nav_date": {"type": "string", "format": "date"}, "share_class": {"type": "string"} }, "required": ["fund_id", "nav_date"] } ), Tool( name="get_fund_holdings", description="Get current fund holdings with market values", inputSchema={ "type": "object", "properties": { "fund_id": {"type": "string"}, "as_of_date": {"type": "string", "format": "date"} }, "required": ["fund_id", "as_of_date"] } ), Tool( name="validate_pricing", description="Validate security pricing for NAV calculation", inputSchema={ "type": "object", "properties": { "security_ids": {"type": "array", "items": {"type": "string"}}, "pricing_date": {"type": "string", "format": "date"} }, "required": ["security_ids", "pricing_date"] } ) ] return base_tools + nav_tools async def _calculate_fund_nav(self, args: Dict[str, Any]) -> List[TextContent]: """Calculate fund NAV""" fund_id = args["fund_id"] nav_date = args["nav_date"] share_class = args.get("share_class") try: # Get fund information fund = next((f for f in self.funds if f.fund_id == fund_id), None) if not fund: return self._create_response({ "error": f"Fund {fund_id} not found" }) # Get fund holdings holdings = await self._get_fund_holdings_data(fund_id, nav_date) # Get security prices security_ids = [h["security_id"] for h in holdings] prices = await self.pricing_service.get_prices(security_ids, nav_date) # Calculate portfolio value total_market_value = Decimal("0") holding_details = [] for holding in holdings: security_id = holding["security_id"] quantity = Decimal(str(holding["quantity"])) price = prices.get(security_id, {}).get("price", Decimal("0")) market_value = quantity * price total_market_value += market_value holding_details.append({ "security_id": security_id, "quantity": float(quantity), "price": float(price), "market_value": float(market_value), "weight": 0 # Will calculate after total is known }) # Calculate weights for holding in holding_details: if total_market_value > 0: holding["weight"] = holding["market_value"] / float(total_market_value) # Get fund expenses and liabilities expenses = await self._get_fund_expenses(fund_id, nav_date) liabilities = await self._get_fund_liabilities(fund_id, nav_date) # Calculate NAV net_assets = total_market_value - expenses - liabilities # Get shares outstanding if share_class: shares_outstanding = await self._get_shares_outstanding(fund_id, share_class, nav_date) nav_per_share = net_assets / shares_outstanding if shares_outstanding > 0 else Decimal("0") nav_result = { "fund_id": fund_id, "share_class": share_class, "nav_date": nav_date, "total_market_value": float(total_market_value), "expenses": float(expenses), "liabilities": float(liabilities), "net_assets": float(net_assets), "shares_outstanding": float(shares_outstanding), "nav_per_share": float(nav_per_share), "holdings": holding_details } else: # Calculate for all share classes share_classes = await self._get_fund_share_classes(fund_id) nav_by_class = {} for sc in share_classes: shares = await self._get_shares_outstanding(fund_id, sc.class_name, nav_date) nav_per_share = net_assets / shares if shares > 0 else Decimal("0") nav_by_class[sc.class_name] = { "shares_outstanding": float(shares), "nav_per_share": float(nav_per_share) } nav_result = { "fund_id": fund_id, "nav_date": nav_date, "total_market_value": float(total_market_value), "expenses": float(expenses), "liabilities": float(liabilities), "net_assets": float(net_assets), "nav_by_share_class": nav_by_class, "holdings": holding_details } return self._create_response(nav_result) except Exception as e: return self._create_response({ "error": f"NAV calculation failed: {str(e)}" }) async def _get_fund_holdings_data(self, fund_id: str, as_of_date: str) -> List[Dict]: """Get fund holdings from database""" query = """ SELECT security_id, quantity, original_cost FROM portfolio_positions WHERE fund_id = %s AND position_date = %s AND quantity > 0 """ return await self.db.fetch_all(query, fund_id, as_of_date) ``` ### Performance Attribution ```python class PerformanceAttributionMCPServer(NAVCalculationMCPServer): def __init__(self, db_connection, risk_service, compliance_service, pricing_service, attribution_service): self.attribution_service = attribution_service super().__init__(db_connection, risk_service, compliance_service, pricing_service) self._setup_attribution_tools() def _setup_attribution_tools(self): """Add performance attribution tools""" @self.server.list_tools() async def enhanced_list_tools(): base_tools = await super().list_tools() attribution_tools = [ Tool( name="calculate_performance_attribution", description="Calculate performance attribution vs benchmark", inputSchema={ "type": "object", "properties": { "fund_id": {"type": "string"}, "start_date": {"type": "string", "format": "date"}, "end_date": {"type": "string", "format": "date"}, "attribution_method": {"type": "string", "enum": ["BRINSON", "FAMA_FRENCH", "CUSTOM"]} }, "required": ["fund_id", "start_date", "end_date"] } ), Tool( name="calculate_fund_returns", description="Calculate fund returns over specified period", inputSchema={ "type": "object", "properties": { "fund_id": {"type": "string"}, "start_date": {"type": "string", "format": "date"}, "end_date": {"type": "string", "format": "date"}, "return_frequency": {"type": "string", "enum": ["DAILY", "MONTHLY", "QUARTERLY"]} }, "required": ["fund_id", "start_date", "end_date"] } ) ] return base_tools + attribution_tools async def _calculate_performance_attribution(self, args: Dict[str, Any]) -> List[TextContent]: """Calculate performance attribution analysis""" fund_id = args["fund_id"] start_date = args["start_date"] end_date = args["end_date"] attribution_method = args.get("attribution_method", "BRINSON") try: # Get fund and benchmark information fund = next((f for f in self.funds if f.fund_id == fund_id), None) if not fund: return self._create_response({"error": f"Fund {fund_id} not found"}) benchmark = next((b for b in self.benchmarks if b.benchmark_id == fund.benchmark_id), None) if not benchmark: return self._create_response({"error": f"Benchmark {fund.benchmark_id} not found"}) # Calculate fund returns fund_returns = await self._calculate_period_returns(fund_id, start_date, end_date) # Get benchmark returns benchmark_returns = await self._get_benchmark_returns(fund.benchmark_id, start_date, end_date) # Calculate attribution components attribution_result = await self.attribution_service.calculate_attribution( fund_id=fund_id, benchmark_id=fund.benchmark_id, start_date=start_date, end_date=end_date, method=attribution_method ) result = { "fund_id": fund_id, "benchmark_id": fund.benchmark_id, "period": f"{start_date} to {end_date}", "fund_return": fund_returns["total_return"], "benchmark_return": benchmark_returns["total_return"], "excess_return": fund_returns["total_return"] - benchmark_returns["total_return"], "attribution_method": attribution_method, "attribution_analysis": attribution_result } return self._create_response(result) except Exception as e: return self._create_response({ "error": f"Performance attribution calculation failed: {str(e)}" }) ``` ## Testing Strategy ### Unit Tests for Fund Accounting ```python import pytest import asyncio from decimal import Decimal from datetime import date from unittest.mock import Mock, patch from fund_accounting_mcp_server import FundAccountingMCPServer, Fund, FundType, AssetClass class TestFundAccountingMCPServer: @pytest.fixture def server(self): return FundAccountingMCPServer() @pytest.fixture def sample_fund(self): return Fund( fund_id="TEST001", fund_name="Test Fund", fund_type=FundType.MUTUAL_FUND, asset_class=AssetClass.EQUITY, inception_date=date(2020, 1, 1), base_currency="USD", expense_ratio=Decimal("0.0075"), management_fee=Decimal("0.0050"), benchmark_id="BENCH001", nav_frequency="DAILY", is_active=True, regulatory_classification="40_ACT", investment_objective="Test objective", minimum_investment=Decimal("10000"), share_classes=["A", "I"] ) @pytest.mark.asyncio async def test_validate_fund_trade_valid(self, server): """Test validation of a valid fund trade""" args = { "fund_id": "FUND001", "security_id": "SEC001", "quantity": 1000, "trade_type": "BUY", "trade_amount": 150000.0 } result = await server._validate_fund_trade(args) result_data = json.loads(result[0].text) assert result_data["valid"] is True assert "fund" in result_data assert "security" in result_data assert result_data["fund"]["fund_id"] == "FUND001" @pytest.mark.asyncio async def test_validate_fund_trade_invalid_fund(self, server): """Test validation with invalid fund ID""" args = { "fund_id": "INVALID", "security_id": "SEC001", "quantity": 1000, "trade_type": "BUY" } result = await server._validate_fund_trade(args) result_data = json.loads(result[0].text) assert result_data["valid"] is False assert "not found" in result_data["error"] @pytest.mark.asyncio async def test_get_fund_benchmark(self, server): """Test benchmark retrieval for fund""" args = {"fund_id": "FUND001"} result = await server._get_fund_benchmark(args) result_data = json.loads(result[0].text) assert "benchmark" in result_data assert result_data["fund_id"] == "FUND001" assert result_data["benchmark"]["benchmark_id"] == "BENCH001" @pytest.mark.asyncio async def test_lookup_security_by_isin(self, server): """Test security lookup by ISIN""" args = { "identifier": "US0378331005", "identifier_type": "ISIN" } result = await server._lookup_security_details(args) result_data = json.loads(result[0].text) assert result_data["found"] is True assert result_data["security"]["security_name"] == "Apple Inc." assert result_data["security"]["identifiers"]["ISIN"] == "US0378331005" @pytest.mark.asyncio async def test_calculate_fund_metrics(self, server): """Test fund metrics calculation""" args = { "fund_id": "FUND001", "calculation_date": "2023-12-31", "metrics": ["expense_ratio", "management_fee"] } result = await server._calculate_fund_metrics(args) result_data = json.loads(result[0].text) assert "metrics" in result_data assert "expense_ratio" in result_data["metrics"] assert "management_fee" in result_data["metrics"] assert result_data["metrics"]["expense_ratio"] == 0.0075 ``` ### Integration Tests for Fund Operations ```python import pytest import asyncio from mcp_test_client import MCPTestClient class TestFundAccountingMCPIntegration: @pytest.fixture def mcp_client(self): return MCPTestClient("fund-accounting-static-data") @pytest.mark.asyncio async def test_complete_fund_trade_workflow(self, mcp_client): """Test complete fund trade validation workflow""" # Step 1: Validate the trade validation_result = await mcp_client.call_tool( "validate_fund_trade", { "fund_id": "FUND001", "security_id": "SEC001", "quantity": 500, "trade_type": "BUY", "trade_amount": 75000.0 } ) assert validation_result["valid"] is True assert validation_result["fund"]["asset_class"] == "EQUITY" # Step 2: Get benchmark information benchmark_result = await mcp_client.call_tool( "get_fund_benchmark", {"fund_id": "FUND001"} ) assert "benchmark" in benchmark_result assert benchmark_result["benchmark"]["asset_class"] == "EQUITY" # Step 3: Get detailed security information security_result = await mcp_client.call_tool( "lookup_security_details", { "identifier": "SEC001", "identifier_type": "INTERNAL_ID" } ) assert security_result["found"] is True assert security_result["security"]["asset_class"] == "EQUITY" assert "pricing_rule" in security_result @pytest.mark.asyncio async def test_etf_vs_mutual_fund_characteristics(self, mcp_client): """Test differences between ETF and mutual fund handling""" # Test mutual fund mf_validation = await mcp_client.call_tool( "validate_fund_trade", { "fund_id": "FUND001", # Mutual Fund "security_id": "SEC001", "quantity": 100, "trade_type": "BUY" } ) # Test ETF etf_validation = await mcp_client.call_tool( "validate_fund_trade", { "fund_id": "ETF001", # ETF "security_id": "SEC003", "quantity": 100, "trade_type": "BUY" } ) assert mf_validation["fund"]["fund_type"] == "MUTUAL_FUND" assert etf_validation["fund"]["fund_type"] == "ETF" # ETF should have lower expense ratio assert etf_validation["fund"]["expense_ratio"] < mf_validation["fund"]["expense_ratio"] ``` ### Performance Tests ```python import asyncio import time import statistics from concurrent.futures import ThreadPoolExecutor class FundAccountingLoadTester: def __init__(self, mcp_client, num_concurrent_requests=100): self.mcp_client = mcp_client self.num_concurrent_requests = num_concurrent_requests self.results = [] async def single_fund_lookup(self): """Simulate a single fund lookup request""" start_time = time.time() try: result = await self.mcp_client.call_tool( "lookup_security_details", { "identifier": "US0378331005", "identifier_type": "ISIN" } ) duration = time.time() - start_time self.results.append({ "success": True, "duration": duration, "response_size": len(str(result)) }) except Exception as e: duration = time.time() - start_time self.results.append({ "success": False, "duration": duration, "error": str(e) }) async def single_trade_validation(self): """Simulate a single trade validation request""" start_time = time.time() try: result = await self.mcp_client.call_tool( "validate_fund_trade", { "fund_id": "FUND001", "security_id": "SEC001", "quantity": 100, "trade_type": "BUY" } ) duration = time.time() - start_time self.results.append({ "success": True, "duration": duration, "operation": "trade_validation" }) except Exception as e: duration = time.time() - start_time self.results.append({ "success": False, "duration": duration, "error": str(e), "operation": "trade_validation" }) async def run_mixed_load_test(self, duration_seconds=300): """Run mixed load test with various operations""" end_time = time.time() + duration_seconds while time.time() < end_time: # Mix of different operations tasks = [] # 50% trade validations for _ in range(self.num_concurrent_requests // 2): tasks.append(self.single_trade_validation()) # 30% security lookups for _ in range(int(self.num_concurrent_requests * 0.3)): tasks.append(self.single_fund_lookup()) # 20% benchmark lookups for _ in range(int(self.num_concurrent_requests * 0.2)): tasks.append(self.single_benchmark_lookup()) await asyncio.gather(*tasks) await asyncio.sleep(0.1) # Brief pause between batches self.print_detailed_results() def print_detailed_results(self): """Print detailed load test results""" successful_requests = [r for r in self.results if r["success"]] failed_requests = [r for r in self.results if not r["success"]] if successful_requests: durations = [r["duration"] for r in successful_requests] print(f"Fund Accounting MCP Load Test Results:") print(f" Total Requests: {len(self.results)}") print(f" Successful: {len(successful_requests)}") print(f" Failed: {len(failed_requests)}") print(f" Success Rate: {len(successful_requests)/len(self.results)*100:.2f}%") print(f" Average Response Time: {statistics.mean(durations):.3f}s") print(f" Median Response Time: {statistics.median(durations):.3f}s") print(f" 95th Percentile: {statistics.quantiles(durations, n=20)[18]:.3f}s") print(f" 99th Percentile: {statistics.quantiles(durations, n=100)[98]:.3f}s") print(f" Requests/Second: {len(self.results)/300:.2f}") # Break down by operation type operations = {} for result in successful_requests: op_type = result.get("operation", "unknown") if op_type not in operations: operations[op_type] = [] operations[op_type].append(result["duration"]) print(f"\n Performance by Operation Type:") for op_type, times in operations.items(): print(f" {op_type}: avg {statistics.mean(times):.3f}s, " f"95th {statistics.quantiles(times, n=20)[18]:.3f}s") ``` ## Deployment ### Docker Configuration for Fund Accounting ```dockerfile # Dockerfile FROM python:3.11-slim WORKDIR /app # Install system dependencies for financial calculations RUN apt-get update && apt-get install -y \ gcc \ g++ \ gfortran \ libopenblas-dev \ liblapack-dev \ && rm -rf /var/lib/apt/lists/* # Copy requirements and install Python dependencies COPY requirements.txt . RUN pip install --no-cache-dir -r requirements.txt # Copy application code COPY . . # Create non-root user RUN useradd --create-home --shell /bin/bash fundmcp RUN chown -R fundmcp:fundmcp /app USER fundmcp # Health check HEALTHCHECK --interval=30s --timeout=10s --start-period=5s --retries=3 \ CMD python -c "import requests; requests.get('http://localhost:8080/health')" # Expose port EXPOSE 8080 # Run the application CMD ["python", "-m", "uvicorn", "fund_accounting_mcp_server:app", "--host", "0.0.0.0", "--port", "8080"] ``` ### Docker Compose for Investment Management Stack ```yaml # docker-compose.yml version: '3.8' services: fund-accounting-mcp-server: build: . ports: - "8080:8080" environment: - DATABASE_URL=postgresql://funduser:fundpass@postgres:5432/investment_db - REDIS_URL=redis://redis:6379 - PRICING_SERVICE_URL=http://pricing-service:8081 - RISK_SERVICE_URL=http://risk-service:8082 - LOG_LEVEL=INFO - ENVIRONMENT=production depends_on: - postgres - redis - pricing-service - risk-service volumes: - ./config.yaml:/app/config.yaml:ro restart: unless-stopped networks: - fund-network postgres: image: postgres:15 environment: - POSTGRES_DB=investment_db - POSTGRES_USER=funduser - POSTGRES_PASSWORD=fundpass volumes: - postgres_data:/var/lib/postgresql/data - ./sql/init_investment_db.sql:/docker-entrypoint-initdb.d/init.sql networks: - fund-network redis: image: redis:7-alpine command: redis-server --appendonly yes --maxmemory 512mb --maxmemory-policy allkeys-lru volumes: - redis_data:/data networks: - fund-network pricing-service: image: company/pricing-service:latest ports: - "8081:8081" environment: - BLOOMBERG_API_KEY=${BLOOMBERG_API_KEY} - REUTERS_API_KEY=${REUTERS_API_KEY} networks: - fund-network risk-service: image: company/risk-analytics:latest ports: - "8082:8082" environment: - RISK_MODEL_PATH=/models volumes: - ./risk_models:/models:ro networks: - fund-network prometheus: image: prom/prometheus ports: - "9090:9090" volumes: - ./monitoring/prometheus.yml:/etc/prometheus/prometheus.yml networks: - fund-network grafana: image: grafana/grafana ports: - "3000:3000" environment: - GF_SECURITY_ADMIN_PASSWORD=admin volumes: - grafana_data:/var/lib/grafana - ./monitoring/grafana/dashboards:/etc/grafana/provisioning/dashboards networks: - fund-network volumes: postgres_data: redis_data: grafana_data: networks: fund-network: driver: bridge ``` ### Production Database Schema for Investment Management ```sql -- sql/init_investment_db.sql -- Fund Master Tables CREATE TABLE fund_master ( fund_id VARCHAR(20) PRIMARY KEY, fund_name VARCHAR(255) NOT NULL, fund_type VARCHAR(20) NOT NULL, asset_class VARCHAR(30) NOT NULL, inception_date DATE NOT NULL, base_currency CHAR(3) NOT NULL, expense_ratio DECIMAL(8,6) DEFAULT 0, management_fee DECIMAL(8,6) DEFAULT 0, benchmark_id VARCHAR(20), nav_frequency VARCHAR(10) DEFAULT 'DAILY', is_active BOOLEAN DEFAULT true, regulatory_classification VARCHAR(50), investment_objective TEXT, minimum_investment DECIMAL(15,2) DEFAULT 0, created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP, updated_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP ); CREATE TABLE share_classes ( share_class_id VARCHAR(30) PRIMARY KEY, fund_id VARCHAR(20) NOT NULL REFERENCES fund_master(fund_id), class_name VARCHAR(50) NOT NULL, ticker_symbol VARCHAR(10), isin VARCHAR(12), cusip VARCHAR(9), expense_ratio DECIMAL(8,6) DEFAULT 0, management_fee DECIMAL(8,6) DEFAULT 0, distribution_fee DECIMAL(8,6) DEFAULT 0, load_front DECIMAL(6,4) DEFAULT 0, load_back DECIMAL(6,4) DEFAULT 0, minimum_investment DECIMAL(15,2) DEFAULT 0, currency CHAR(3) NOT NULL, is_active BOOLEAN DEFAULT true, created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP, updated_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP ); CREATE TABLE benchmarks ( benchmark_id VARCHAR(20) PRIMARY KEY, benchmark_name VARCHAR(255) NOT NULL, provider VARCHAR(100), asset_class VARCHAR(30) NOT NULL, currency CHAR(3) NOT NULL, pricing_source VARCHAR(50), calculation_method VARCHAR(100), is_active BOOLEAN DEFAULT true, constituent_count INTEGER, created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP, updated_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP ); CREATE TABLE security_master ( security_id VARCHAR(20) PRIMARY KEY, security_name VARCHAR(255) NOT NULL, asset_class VARCHAR(30) NOT NULL, security_type VARCHAR(50) NOT NULL, currency CHAR(3) NOT NULL, country_of_risk CHAR(2), sector VARCHAR(100), industry VARCHAR(100), pricing_source VARCHAR(50), exchange VARCHAR(50), is_active BOOLEAN DEFAULT true, created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP, updated_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP ); CREATE TABLE security_identifiers ( security_id VARCHAR(20) NOT NULL REFERENCES security_master(security_id), identifier_type VARCHAR(20) NOT NULL, identifier_value VARCHAR(50) NOT NULL, is_primary BOOLEAN DEFAULT false, created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP, PRIMARY KEY (security_id, identifier_type) ); CREATE TABLE pricing_rules ( rule_id VARCHAR(20) PRIMARY KEY, security_id VARCHAR(20) NOT NULL REFERENCES security_master(security_id), pricing_source VARCHAR(50) NOT NULL, pricing_method VARCHAR(50) NOT NULL, stale_price_threshold_hours INTEGER DEFAULT 24, is_active BOOLEAN DEFAULT true, created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP, updated_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP ); CREATE TABLE pricing_validation_rules ( rule_id VARCHAR(20) NOT NULL REFERENCES pricing_rules(rule_id), validation_type VARCHAR(50) NOT NULL, validation_parameter JSONB, is_active BOOLEAN DEFAULT true, PRIMARY KEY (rule_id, validation_type) ); CREATE TABLE pricing_fallback_sources ( rule_id VARCHAR(20) NOT NULL REFERENCES pricing_rules(rule_id), fallback_source VARCHAR(50) NOT NULL, priority_order INTEGER NOT NULL, PRIMARY KEY (rule_id, fallback_source) ); -- Portfolio Holdings and NAV Tables CREATE TABLE portfolio_positions ( position_id SERIAL PRIMARY KEY, fund_id VARCHAR(20) NOT NULL REFERENCES fund_master(fund_id), security_id VARCHAR(20) NOT NULL REFERENCES security_master(security_id), position_date DATE NOT NULL, quantity DECIMAL(18,6) NOT NULL, original_cost DECIMAL(18,4), market_value DECIMAL(18,4), weight_percent DECIMAL(8,6), created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP, UNIQUE(fund_id, security_id, position_date) ); CREATE TABLE fund_nav ( nav_id SERIAL PRIMARY KEY, fund_id VARCHAR(20) NOT NULL REFERENCES fund_master(fund_id), share_class_id VARCHAR(30) REFERENCES share_classes(share_class_id), nav_date DATE NOT NULL, total_assets DECIMAL(18,4) NOT NULL, total_liabilities DECIMAL(18,4) DEFAULT 0, net_assets DECIMAL(18,4) NOT NULL, shares_outstanding DECIMAL(18,6) NOT NULL, nav_per_share DECIMAL(12,6) NOT NULL, created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP, UNIQUE(fund_id, share_class_id, nav_date) ); CREATE TABLE fund_expenses ( expense_id SERIAL PRIMARY KEY, fund_id VARCHAR(20) NOT NULL REFERENCES fund_master(fund_id), expense_date DATE NOT NULL, expense_type VARCHAR(50) NOT NULL, expense_amount DECIMAL(12,4) NOT NULL, description TEXT, created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP ); -- Investment Restrictions Tables CREATE TABLE fund_restrictions ( restriction_id SERIAL PRIMARY KEY, fund_id VARCHAR(20) NOT NULL REFERENCES fund_master(fund_id), restriction_type VARCHAR(50) NOT NULL, restriction_value DECIMAL(8,4), restriction_config JSONB, is_active BOOLEAN DEFAULT true, effective_date DATE NOT NULL, expiration_date DATE, created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP ); -- Performance and Attribution Tables CREATE TABLE fund_returns ( return_id SERIAL PRIMARY KEY, fund_id VARCHAR(20) NOT NULL REFERENCES fund_master(fund_id), share_class_id VARCHAR(30) REFERENCES share_classes(share_class_id), return_date DATE NOT NULL, return_1d DECIMAL(10,8), return_mtd DECIMAL(10,8), return_qtd DECIMAL(10,8), return_ytd DECIMAL(10,8), return_1y DECIMAL(10,8), return_3y DECIMAL(10,8), return_5y DECIMAL(10,8), return_inception DECIMAL(10,8), created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP, UNIQUE(fund_id, share_class_id, return_date) ); CREATE TABLE benchmark_returns ( return_id SERIAL PRIMARY KEY, benchmark_id VARCHAR(20) NOT NULL REFERENCES benchmarks(benchmark_id), return_date DATE NOT NULL, return_1d DECIMAL(10,8), return_mtd DECIMAL(10,8), return_qtd DECIMAL(10,8), return_ytd DECIMAL(10,8), return_1y DECIMAL(10,8), return_3y DECIMAL(10,8), return_5y DECIMAL(10,8), return_inception DECIMAL(10,8), index_level DECIMAL(18,6), created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP, UNIQUE(benchmark_id, return_date) ); -- Risk Analytics Tables CREATE TABLE fund_risk_metrics ( metric_id SERIAL PRIMARY KEY, fund_id VARCHAR(20) NOT NULL REFERENCES fund_master(fund_id), calculation_date DATE NOT NULL, var_1d DECIMAL(12,8), var_10d DECIMAL(12,8), tracking_error DECIMAL(10,8), beta DECIMAL(8,6), alpha DECIMAL(10,8), sharpe_ratio DECIMAL(8,6), information_ratio DECIMAL(8,6), max_drawdown DECIMAL(8,6), volatility DECIMAL(10,8), created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP, UNIQUE(fund_id, calculation_date) ); -- Security Prices Table CREATE TABLE security_prices ( price_id SERIAL PRIMARY KEY, security_id VARCHAR(20) NOT NULL REFERENCES security_master(security_id), price_date DATE NOT NULL, price DECIMAL(18,8) NOT NULL, currency CHAR(3) NOT NULL, pricing_source VARCHAR(50) NOT NULL, price_type VARCHAR(20) DEFAULT 'CLOSE', volume BIGINT, is_validated BOOLEAN DEFAULT false, created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP, UNIQUE(security_id, price_date, pricing_source, price_type) ); -- Indexes for Performance CREATE INDEX idx_portfolio_positions_fund_date ON portfolio_positions(fund_id, position_date); CREATE INDEX idx_portfolio_positions_security_date ON portfolio_positions(security_id, position_date); CREATE INDEX idx_fund_nav_fund_date ON fund_nav(fund_id, nav_date); CREATE INDEX idx_security_prices_security_date ON security_prices(security_id, price_date); CREATE INDEX idx_fund_returns_fund_date ON fund_returns(fund_id, return_date); CREATE INDEX idx_benchmark_returns_bench_date ON benchmark_returns(benchmark_id, return_date); -- Sample Data INSERT INTO fund_master VALUES ('FUND001', 'Global Equity Growth Fund', 'MUTUAL_FUND', 'EQUITY', '2010-01-15', 'USD', 0.007500, 0.005000, 'BENCH001', 'DAILY', true, '40_ACT', 'Long-term capital growth through global equity investments', 10000.00), ('ETF001', 'Technology Sector ETF', 'ETF', 'EQUITY', '2015-06-01', 'USD', 0.002500, 0.002000, 'BENCH002', 'DAILY', true, '40_ACT', 'Track the performance of technology sector stocks', 1.00), ('FUND002', 'International Bond Fund', 'MUTUAL_FUND', 'FIXED_INCOME', '2012-03-20', 'USD', 0.006000, 0.004000, 'BENCH003', 'DAILY', true, '40_ACT', 'Income generation through international fixed income securities', 5000.00); INSERT INTO share_classes VALUES ('FUND001_A', 'FUND001', 'Class A', 'GEGRX', 'US12345678901', '123456789', 0.010000, 0.005000, 0.002500, 0.0575, 0.0000, 10000.00, 'USD', true), ('FUND001_I', 'FUND001', 'Institutional', 'GEGIX', 'US12345678902', '123456790', 0.007500, 0.005000, 0.000000, 0.0000, 0.0000, 1000000.00, 'USD', true), ('ETF001_ETF', 'ETF001', 'ETF Shares', 'TECH', 'US12345678903', '123456791', 0.002500, 0.002000, 0.000000, 0.0000, 0.0000, 1.00, 'USD', true); INSERT INTO benchmarks VALUES ('BENCH001', 'MSCI World Index', 'MSCI', 'EQUITY', 'USD', 'MSCI', 'MARKET_CAP_WEIGHTED', true, 1500), ('BENCH002', 'NASDAQ Technology Index', 'NASDAQ', 'EQUITY', 'USD', 'BLOOMBERG', 'MARKET_CAP_WEIGHTED', true, 100), ('BENCH003', 'Bloomberg Global Aggregate Bond Index', 'Bloomberg', 'FIXED_INCOME', 'USD', 'BLOOMBERG', 'MARKET_VALUE_WEIGHTED', true, 25000); INSERT INTO security_master VALUES ('SEC001', 'Apple Inc.', 'EQUITY', 'COMMON_STOCK', 'USD', 'US', 'Technology', 'Technology Hardware', 'BLOOMBERG', 'NASDAQ', true), ('SEC002', 'US Treasury 2.5% 2030', 'FIXED_INCOME', 'GOVERNMENT_BOND', 'USD', 'US', 'Government', 'Treasury', 'BLOOMBERG', null, true), ('SEC003', 'Microsoft Corporation', 'EQUITY', 'COMMON_STOCK', 'USD', 'US', 'Technology', 'Software', 'BLOOMBERG', 'NASDAQ', true); INSERT INTO security_identifiers VALUES ('SEC001', 'ISIN', 'US0378331005', true), ('SEC001', 'CUSIP', '037833100', false), ('SEC001', 'TICKER', 'AAPL', false), ('SEC001', 'SEDOL', '2046251', false), ('SEC002', 'ISIN', 'US912828XG55', true), ('SEC002', 'CUSIP', '912828XG5', false), ('SEC002', 'TICKER', 'T 2.5 08/15/30', false), ('SEC003', 'ISIN', 'US5949181045', true), ('SEC003', 'CUSIP', '594918104', false), ('SEC003', 'TICKER', 'MSFT', false), ('SEC003', 'SEDOL', '2588173', false); INSERT INTO pricing_rules VALUES ('RULE001', 'SEC001', 'BLOOMBERG', 'MARKET', 24, true), ('RULE002', 'SEC002', 'BLOOMBERG', 'MATRIX', 24, true), ('RULE003', 'SEC003', 'BLOOMBERG', 'MARKET', 24, true); INSERT INTO pricing_validation_rules VALUES ('RULE001', 'STALE_PRICE_CHECK', '{"max_hours": 24}', true), ('RULE001', 'PRICE_VARIANCE_CHECK', '{"max_variance_pct": 10.0}', true), ('RULE002', 'YIELD_CURVE_CHECK', '{"tolerance_bps": 5}', true), ('RULE002', 'CREDIT_SPREAD_CHECK', '{"tolerance_bps": 10}', true), ('RULE003', 'STALE_PRICE_CHECK', '{"max_hours": 24}', true), ('RULE003', 'VOLUME_CHECK', '{"min_volume": 10000}', true); INSERT INTO pricing_fallback_sources VALUES ('RULE001', 'REUTERS', 1), ('RULE001', 'FACTSET', 2), ('RULE002', 'REUTERS', 1), ('RULE003', 'REUTERS', 1), ('RULE003', 'FACTSET', 2); -- Sample fund restrictions INSERT INTO fund_restrictions VALUES (1, 'FUND001', 'MAX_SINGLE_SECURITY_WEIGHT', 5.0, null, true, '2010-01-15', null), (2, 'FUND001', 'MAX_SECTOR_WEIGHT', 25.0, null, true, '2010-01-15', null), (3, 'FUND001', 'MAX_COUNTRY_WEIGHT', 30.0, null, true, '2010-01-15', null), (4, 'FUND001', 'MIN_HOLDINGS', 50, null, true, '2010-01-15', null), (5, 'ETF001', 'MAX_SINGLE_SECURITY_WEIGHT', 25.0, null, true, '2015-06-01', null), (6, 'ETF001', 'TRACKING_ERROR_LIMIT', 0.5, null, true, '2015-06-01', null), (7, 'FUND002', 'MIN_CREDIT_QUALITY', null, '{"min_rating": "BBB"}', true, '2012-03-20', null), (8, 'FUND002', 'MAX_DURATION', 10.0, null, true, '2012-03-20', null); ``` ### Kubernetes Deployment for Fund Accounting ```yaml # k8s-fund-accounting.yaml apiVersion: apps/v1 kind: Deployment metadata: name: fund-accounting-mcp-server labels: app: fund-accounting-mcp-server tier: api spec: replicas: 3 selector: matchLabels: app: fund-accounting-mcp-server template: metadata: labels: app: fund-accounting-mcp-server spec: containers: - name: fund-accounting-mcp-server image: company/fund-accounting-mcp:latest ports: - containerPort: 8080 env: - name: DATABASE_URL valueFrom: secretKeyRef: name: fund-mcp-secrets key: database-url - name: REDIS_URL valueFrom: secretKeyRef: name: fund-mcp-secrets key: redis-url - name: PRICING_SERVICE_URL value: "http://pricing-service:8081" - name: RISK_SERVICE_URL value: "http://risk-service:8082" resources: requests: memory: "512Mi" cpu: "500m" limits: memory: "1Gi" cpu: "1000m" livenessProbe: httpGet: path: /health port: 8080 initialDelaySeconds: 60 periodSeconds: 30 readinessProbe: httpGet: path: /ready port: 8080 initialDelaySeconds: 10 periodSeconds: 5 volumeMounts: - name: config-volume mountPath: /app/config.yaml subPath: config.yaml volumes: - name: config-volume configMap: name: fund-mcp-config --- apiVersion: v1 kind: Service metadata: name: fund-accounting-mcp-service spec: selector: app: fund-accounting-mcp-server ports: - protocol: TCP port: 80 targetPort: 8080 type: ClusterIP --- apiVersion: v1 kind: ConfigMap metadata: name: fund-mcp-config data: config.yaml: | server: name: "fund-accounting-static-data" version: "1.0.0" environment: "production" port: 8080 database: pool_size: 20 timeout: 30 retry_attempts: 3 cache: ttl_minutes: 15 max_connections: 20 security: require_api_key: true rate_limit_per_minute: 2000 allowed_origins: - "https://portfolio-management.company.com" - "https://fund-accounting.company.com" - "https://risk-analytics.company.com" features: enable_real_time_pricing: true enable_risk_analytics: true enable_compliance_checking: true enable_performance_attribution: true monitoring: metrics_enabled: true log_level: "INFO" health_check_interval: 30 --- apiVersion: v1 kind: Secret metadata: name: fund-mcp-secrets type: Opaque stringData: database-url: "postgresql://funduser:fundpass@postgres-service:5432/investment_db" redis-url: "redis://redis-service:6379" ``` ### Requirements File for Investment Management ```txt # requirements.txt # MCP and Core Framework mcp>=1.0.0 asyncio-mqtt>=0.16.0 fastapi>=0.104.0 uvicorn>=0.24.0 pydantic>=2.4.0 # Database and Caching asyncpg>=0.29.0 SQLAlchemy>=2.0.0 alembic>=1.12.0 aioredis>=2.0.0 # Financial and Mathematical Libraries numpy>=1.24.0 pandas>=2.1.0 scipy>=1.11.0 pyquantlib>=0.2.0 # Data Validation and Serialization pydantic>=2.4.0 marshmallow>=3.20.0 # API and HTTP aiohttp>=3.8.0 httpx>=0.24.0 requests>=2.31.0 # Configuration and Environment pyyaml>=6.0 python-dotenv>=1.0.0 # Monitoring and Logging prometheus-client>=0.17.0 structlog>=23.1.0 # Testing pytest>=7.4.0 pytest-asyncio>=0.21.0 pytest-mock>=3.11.0 factory-boy>=3.3.0 # Financial Data Sources (Optional - based on vendors used) # bloomberg-api>=1.0.0 # refinitiv-dataplatform>=1.0.0 # alpha-vantage>=2.3.0 # Development Tools black>=23.7.0 flake8>=6.0.0 mypy>=1.5.0 ``` ## Monitoring and Alerting ### Grafana Dashboard Configuration ```json { "dashboard": { "title": "Fund Accounting MCP Server", "panels": [ { "title": "Fund Trade Validations", "type": "graph", "targets": [ { "expr": "rate(mcp_fund_trade_validations_total[5m])", "legendFormat": "{{status}}" } ] }, { "title": "NAV Calculations", "type": "graph", "targets": [ { "expr": "rate(mcp_nav_calculations_total[5m])", "legendFormat": "{{fund_type}}" } ] }, { "title": "Security Lookups", "type": "graph", "targets": [ { "expr": "histogram_quantile(0.95, rate(mcp_security_lookup_duration_seconds_bucket[5m]))", "legendFormat": "95th Percentile" } ] }, { "title": "Fund Performance Attribution", "type": "graph", "targets": [ { "expr": "rate(mcp_performance_attribution_requests_total[5m])", "legendFormat": "{{attribution_method}}" } ] }, { "title": "Database Connection Pool", "type": "graph", "targets": [ { "expr": "mcp_db_connections_active", "legendFormat": "Active Connections" } ] } ] } } ``` ## Conclusion This comprehensive guide demonstrates how to build a production-ready MCP server specifically designed for mutual fund and ETF accounting static data. The implementation provides: ### Key Benefits for Investment Management - **Comprehensive Fund Data**: Complete fund specifications, share classes, benchmarks, and security master data - **Real-time Validation**: Trade validation against fund restrictions, asset allocation limits, and regulatory requirements - **Performance Analytics**: NAV calculation, performance attribution, and risk analytics integration - **Regulatory Compliance**: Built-in compliance checking for investment restrictions and regulatory requirements - **Scalable Architecture**: Designed to handle high-frequency trading systems and large fund complexes ### Production Features for Investment Firms - **Multi-vendor Pricing**: Support for Bloomberg, Reuters, FactSet, and custom pricing sources - **Risk Integration**: Real-time risk analytics including VaR, tracking error, and attribution analysis - **Fund Accounting Workflows**: Complete NAV calculation, expense allocation, and performance measurement - **Security Master Management**: Comprehensive security identifiers, classifications, and pricing rules - **Investment Restrictions**: Flexible rule engine for fund-specific investment guidelines and limits ### Advanced Investment Management Capabilities - **Real-time Portfolio Monitoring**: Live position tracking and compliance monitoring - **Performance Attribution**: Multi-factor performance analysis and benchmark comparison - **Risk Analytics**: Integration with risk management systems for VaR and stress testing - **Regulatory Reporting**: Support for various regulatory frameworks (40 Act, UCITS, etc.) - **Multi-currency Support**: Global fund management with currency hedging capabilities ### For Investment Management AI Agents The MCP server provides a robust foundation for building sophisticated investment management agents that can: - Validate trades against complex fund restrictions and regulatory requirements - Calculate NAV and performance metrics in real-time - Perform risk analysis and attribution analysis - Monitor compliance with investment guidelines - Generate regulatory reports and client communications This architecture enables investment management firms to build an ecosystem of AI agents that work together seamlessly, all backed by consistent, reliable fund accounting and investment data services. The system is designed to scale from small asset managers to large global investment firms with complex multi-fund, multi-currency operations. ## References ### Technical Standards and Protocols 1. **Model Context Protocol (MCP) Specification** - Anthropic. (2024). *Model Context Protocol Documentation*. https://docs.anthropic.com/en/api/mcp - GitHub Repository: https://github.com/modelcontextprotocol/specification 2. **Financial Industry Standards** - **ISO 20022**: *Universal Financial Industry Message Scheme*. International Organization for Standardization. https://www.iso20022.org/ - **FIX Protocol**: *Financial Information eXchange Protocol for Electronic Trading*. FIX Trading Community. https://www.fixtrading.org/ - **SWIFT Standards**: *Society for Worldwide Interbank Financial Telecommunication*. https://www.swift.com/standards ### Investment Management Frameworks 3. **Fund Accounting Standards** - Investment Company Institute. (2023). *Mutual Fund Fact Book*. https://www.ici.org/system/files/2023-05/2023_factbook.pdf - CFA Institute. (2020). *Global Investment Performance Standards (GIPS®)*. https://www.cfainstitute.org/en/ethics-standards/codes/gips-standards 4. **Regulatory Guidelines** - U.S. Securities and Exchange Commission. *Investment Company Act of 1940*. https://www.sec.gov/investment/laws-regulations - European Securities and Markets Authority. *UCITS Directive*. https://www.esma.europa.eu/regulation/fund-management/ucits ### Data Standards and Identifiers 5. **Security Identification Standards** - **ISIN**: International Securities Identification Number. ISO 6166:2021. https://www.isin.org/ - **CUSIP**: Committee on Uniform Securities Identification Procedures. https://www.cusip.com/ - **SEDOL**: Stock Exchange Daily Official List. London Stock Exchange Group. https://www.londonstockexchange.com/ 6. **Classification Systems** - **GICS**: Global Industry Classification Standard. MSCI and S&P Dow Jones Indices. https://www.msci.com/gics - **ICB**: Industry Classification Benchmark. FTSE Russell. https://www.ftserussell.com/data/industry-classification-benchmark-icb ### Technology and Architecture References 7. **Database and Performance** - PostgreSQL Global Development Group. (2023). *PostgreSQL Documentation*. https://www.postgresql.org/docs/ - Redis Labs. (2023). *Redis Documentation*. https://redis.io/documentation 8. **Python Financial Libraries** - McKinney, W. (2022). *Python for Data Analysis, 3rd Edition*. O'Reilly Media. - Hilpisch, Y. (2020). *Python for Finance: Mastering Data-Driven Finance, 2nd Edition*. O'Reilly Media. ### Risk Management and Performance Attribution 9. **Risk Analytics** - Jorion, P. (2020). *Value at Risk: The New Benchmark for Managing Financial Risk, 4th Edition*. McGraw-Hill Education. - Litterman, R. (1996). "Hot Spots and Hedges." *Journal of Portfolio Management*, 23(2), 52-75. 10. **Performance Attribution** - Brinson, G. P., Hood, L. R., & Beebower, G. L. (1986). "Determinants of Portfolio Performance." *Financial Analysts Journal*, 42(4), 39-44. - Fama, E. F., & French, K. R. (1993). "Common Risk Factors in the Returns on Stocks and Bonds." *Journal of Financial Economics*, 33(1), 3-56. ### API Design and Microservices 11. **REST API Design** - Fielding, R. T. (2000). *Architectural Styles and the Design of Network-based Software Architectures*. Doctoral dissertation, University of California, Irvine. - Richardson, L., & Ruby, S. (2007). *RESTful Web Services*. O'Reilly Media. 12. **Microservices Architecture** - Newman, S. (2021). *Building Microservices: Designing Fine-Grained Systems, 2nd Edition*. O'Reilly Media. - Fowler, M. (2014). "Microservices." https://martinfowler.com/articles/microservices.html ### Containerization and Deployment 13. **Docker and Kubernetes** - Burns, B., & Beda, J. (2019). *Kubernetes: Up and Running, 2nd Edition*. O'Reilly Media. - Docker Inc. (2023). *Docker Documentation*. https://docs.docker.com/ ### Monitoring and Observability 14. **Prometheus and Grafana** - Prometheus Authors. (2023). *Prometheus Documentation*. https://prometheus.io/docs/ - Grafana Labs. (2023). *Grafana Documentation*. https://grafana.com/docs/ ### Testing and Quality Assurance 15. **Testing Frameworks** - Percival, H., & Gregory, B. (2020). *Architecture Patterns with Python*. O'Reilly Media. - pytest Development Team. (2023). *pytest Documentation*. https://docs.pytest.org/ ### Financial Data Vendors 16. **Market Data Providers** - **Bloomberg Terminal**: Bloomberg L.P. https://www.bloomberg.com/professional/ - **Refinitiv (formerly Thomson Reuters)**: https://www.refinitiv.com/ - **FactSet**: https://www.factset.com/ - **MSCI**: https://www.msci.com/ ### Compliance and Regulatory Technology 17. **RegTech and Compliance** - Arner, D. W., Barberis, J., & Buckey, R. P. (2017). "FinTech, RegTech, and the Reconceptualization of Financial Regulation." *Northwestern Journal of International Law & Business*, 37(3), 371-413. - Financial Conduct Authority. (2023). *RegTech*. https://www.fca.org.uk/firms/innovation/regtech ### Academic and Research Papers 18. **Fund Performance and Efficiency** - Sharpe, W. F. (1966). "Mutual Fund Performance." *Journal of Business*, 39(1), 119-138. - Jensen, M. C. (1968). "The Performance of Mutual Funds in the Period 1945-1964." *Journal of Finance*, 23(2), 389-416. 19. **Modern Portfolio Theory** - Markowitz, H. (1952). "Portfolio Selection." *Journal of Finance*, 7(1), 77-91. - Sharpe, W. F. (1964). "Capital Asset Prices: A Theory of Market Equilibrium Under Conditions of Risk." *Journal of Finance*, 19(3), 425-442. ### Industry Reports and Whitepapers 20. **Investment Management Technology** - PwC. (2023). *Asset & Wealth Management Revolution: Embracing Exponential Change*. https://www.pwc.com/gx/en/industries/financial-services/publications/asset-wealth-management-revolution.html - Deloitte. (2023). *Investment Management Outlook 2023*. https://www2.deloitte.com/content/dam/Deloitte/us/Documents/financial-services/us-fsi-dcfs-investment-management-outlook-2023.pdf 21. **FinTech and AI in Investment Management** - McKinsey & Company. (2023). *The Future of AI in Financial Services*. https://www.mckinsey.com/industries/financial-services/our-insights/the-future-of-ai-in-financial-services - EY. (2023). *Global FinTech Adoption Index 2023*. https://www.ey.com/en_gl/financial-services-emeia/how-fintech-is-evolving-to-meet-changing-demands ### Open Source Projects and Libraries 22. **Python Financial Ecosystem** - **QuantLib**: https://www.quantlib.org/ - **PyPortfolioOpt**: https://github.com/robertmartin8/PyPortfolioOpt - **zipline**: https://github.com/quantopian/zipline - **pandas**: https://pandas.pydata.org/ 23. **Java Financial Libraries** - **Strata**: OpenGamma's market risk and pricing library. https://github.com/OpenGamma/Strata - **JQuantLib**: Java port of QuantLib. http://www.jquantlib.org/ ### Security and Best Practices 24. **API Security** - OWASP. (2023). *API Security Top 10 2023*. https://owasp.org/API-Security/editions/2023/en/0x00-toc/ - National Institute of Standards and Technology. (2020). *NIST Cybersecurity Framework*. https://www.nist.gov/cyberframework ### Data Privacy and Governance 25. **Data Protection** - European Union. (2018). *General Data Protection Regulation (GDPR)*. https://eur-lex.europa.eu/eli/reg/2016/679/oj - California Consumer Privacy Act. (2020). https://oag.ca.gov/privacy/ccpa --- **Note**: URLs and publication dates are current as of the time of writing (2024). Some links may require institutional access or subscriptions. For academic papers, consider accessing through institutional libraries or academic databases such as JSTOR, SSRN, or Google Scholar. **Disclaimer**: This article is for educational and informational purposes only and does not constitute financial, investment, or professional advice. Implementation of any system described should be done in consultation with qualified professionals and in compliance with applicable regulations and standards.

MCP Server - Reference Implementation in Fund Accounting