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What is MCP?

The Model Context Protocol (MCP) is a standardized protocol that enables AI agents to interact with external tools and data sources. Our Financial MCP Server implements this protocol to provide seamless financial data access to AI systems like Claude.
MCP allows AI agents to call financial tools directly, making it perfect for building AI-powered financial analysis applications.

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How It Works

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MCP vs REST API

MCP Protocol

For AI Agents
  • Native AI integration
  • Structured tool calls
  • Type-safe parameters
  • Error handling optimized for AI

REST API

For Applications
  • Standard HTTP endpoints
  • Web framework integration
  • Frontend/mobile apps
  • Traditional API consumers

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Available MCP Tools

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YFinance Tools (6 tools)

{
  "method": "tools/call",
  "params": {
    "name": "get_stock_quote",
    "arguments": {"symbol": "AAPL"}
  }
}
Available Tools:
  • get_stock_quote - Real-time stock quotes
  • get_company_overview - Company information
  • get_time_series_daily - Historical data
  • search_symbol - Symbol search
  • get_recommendations - Analyst recommendations
  • get_insider_transactions - Insider trading data

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FMP Tools (26 tools)

{
  "method": "tools/call",
  "params": {
    "name": "fmp_get_market_gainers",
    "arguments": {"random_string": "dummy"}
  }
}
Categories:
  • Core Financial: fmp_get_stock_quote, fmp_get_financial_statements, fmp_get_key_metrics, fmp_get_dcf_valuation
  • Market Data: fmp_get_market_gainers, fmp_get_market_losers, fmp_get_most_active, fmp_get_sector_performance
  • Screening: fmp_stock_screener, fmp_search_advanced, fmp_get_stock_news, fmp_get_price_targets
  • Advanced: fmp_get_options_chain, fmp_get_technical_indicators, fmp_get_crypto_prices

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Integration Examples

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Claude Desktop Integration

Add to your Claude Desktop MCP configuration: 
{
  "mcpServers": {
    "financial-data": {
      "command": "python",
      "args": ["/path/to/financial-mcp-server/main.py"],
      "env": {
        "FMP_API_KEY": "your_api_key_here"
      }
    }
  }
}

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Custom AI Agent Integration

import asyncio
from mcp import ClientSession, StdioServerParameters
from mcp.client.stdio import stdio_client

async def call_financial_tool():
    server_params = StdioServerParameters(
        command="python",
        args=["main.py"],
        env={"FMP_API_KEY": "your_key"}
    )
    
    async with stdio_client(server_params) as (read, write):
        async with ClientSession(read, write) as session:
            # Initialize the session
            await session.initialize()
            
            # Call a tool
            result = await session.call_tool(
                "get_stock_quote",
                {"symbol": "AAPL"}
            )
            
            print(f"AAPL Price: ${result.content[0].text}")

asyncio.run(call_financial_tool())

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Tool Response Format

All MCP tools return structured responses: 
{
  "content": [
    {
      "type": "text",
      "text": "{\"symbol\": \"AAPL\", \"price\": 201.08, \"source\": \"FMP\"}"
    }
  ],
  "isError": false
}

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Error Handling

MCP tools provide detailed error information: 
{
  "content": [
    {
      "type": "text", 
      "text": "{\"error\": \"Invalid symbol\", \"detail\": \"Symbol 'INVALID' not found\"}"
    }
  ],
  "isError": true
}

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Best Practices

  • Use FMP tools for reliability and comprehensive data
  • Use YFinance tools for basic data when FMP quota is exceeded
  • Always handle potential rate limiting and errors
  • Validate stock symbols before making calls
  • Use uppercase symbols (AAPL, not aapl)
  • Check required vs optional parameters for each tool
  • Parse JSON responses properly
  • Handle both success and error cases
  • Cache responses when appropriate to reduce API calls
  • Monitor API usage, especially with FMP free tier (250/day)
  • Implement exponential backoff for retries
  • Consider upgrading to paid plans for production use

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Server Configuration

The MCP server automatically starts with both protocols: 
🚀 Starting Enhanced Financial MCP Server
📡 MCP Protocol: http://0.0.0.0:8001/
🌐 REST API: http://0.0.0.0:8001/api/v1/
🔧 Tools Available: 32 (6 YFinance + 26 FMP)

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Troubleshooting

Symptoms: Cannot connect to MCP serverSolutions:
  • Verify server is running on correct port
  • Check environment variables are set
  • Ensure Python dependencies are installed
Symptoms: Tools return errors or timeoutSolutions:
  • Verify FMP API key is valid
  • Check internet connectivity
  • Monitor rate limits
  • Review tool parameters
Symptoms: Cannot parse tool responsesSolutions:
  • Ensure JSON parsing handles both success/error cases
  • Check for nested response structures
  • Validate data types match expectations

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Next Steps

Tool Reference

Complete list of all 32 available MCP tools

Integration Guide

Step-by-step integration with your AI application