mcp-finance-intel

fpcsa/mcp-finance-intel

3.3

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mcp-finance-intel is a Model Context Protocol (MCP) server designed to provide financial intelligence for market analysis and insights.

Tools
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🧠 mcp-finance-intel

A Financial Intelligence MCP Server for market analysis and insights

🚀 Overview

mcp-finance-intel is a Model Context Protocol (MCP) server written in Python, designed to serve as a financial intelligence endpoint for both LLMs and human developers.

It provides structured, insight-rich analytics for crypto (via ccxt) and equities (via yfinance), going far beyond simple price data.

🧩 Features

ToolPurposeExample Usage
quoteGet current prices, 24h change %, and volume for crypto or equities.Latest quote for BTC/USDT and AAPL.
timeseriesFetch OHLCV candles for given symbol and interval.Retrieve 90 days of daily bars.
analyze_assetCompute advanced analytics — trends, risk, indicators — with multiple analysis modes.60-day full analysis of ETH/USDT.

Each tool returns validated JSON outputs, checksums for provenance, and includes "not_investment_advice": true.

🧠 Analysis Modes (for analyze_asset)

ModeDescriptionIncluded Metrics
basicSimple technical overview.SMA(20/50), RSI(14), Volatility, Sharpe, MDD
technicalAdds deeper technical indicators.+ EMA(20/50), MACD(12,26,9), ATR(14), Bollinger(20,2)
risk_plusAdds advanced risk metrics.+ Sortino, VaR(95%), CVaR(95%)
fullCombines both technical + risk metrics.All the above

🏗️ Project Structure

mcp-finance-intel/
├─ adapters/
│  ├─ ccxt_adapter.py        # Crypto data (Binance via ccxt)
│  └─ yfinance_adapter.py    # Equity data (Yahoo Finance)
├─ analytics/
│  ├─ indicators.py          # SMA, EMA, RSI, MACD, ATR, BBands
│  └─ risk.py                # Volatility, Sharpe, Sortino, VaR, CVaR
├─ tools/
│  ├─ quote.py               # Implements quote_tool()
│  ├─ timeseries.py          # Implements timeseries_tool()
│  └─ analyze_asset.py       # Implements analyze_asset_tool()
└─ server.py                 # MCP FastMCP entry point and tool registration

⚙️ Installation

1. Clone the repository

git clone https://github.com/your-org/mcp-finance-intel.git
cd mcp-finance-intel

2. Create a virtual environment

python -m venv .venv
source .venv/bin/activate  # Windows: .venv\Scripts\activate

3. Install dependencies

pip install -r requirements.txt

🧭 Running the MCP Server

▶️ HTTP transport (recommended for testing)

fastmcp run server.py:mcp --transport http --port 8000

Server will be available at: http://localhost:8000

▶️ STDIO transport (for LLM integration)

fastmcp run server.py:mcp --transport stdio

Server will be available at: http://localhost:8000

Example HTTP Payloads

Each request follows JSON-RPC 2.0 format. Ensure you set both:

  • Content-Type: application/json
  • Accept: application/json

Quote

POST -> http://localhost:8000/mcp
Body

{
    "jsonrpc": "2.0",
    "id": 1,
    "method": "tools/call",
    "params": {
        "name": "quote",
        "arguments": {
            "input": {
                "symbols": ["BTC/USDT", "AAPL"]
            }
        }
    }
}

Response

{
  "result": {
    "structuredContent": {
      "results": [
        {
          "symbol": "BTC/USDT",
          "last": 104512.25,
          "change_24h_pct": -2.35,
          "volume_24h": 38512.7,
          "source": "ccxt:binance",
          "asof": "2025-10-20T11:35:00+00:00",
          "bars_used": 1,
          "checksum": "..."
        },
        {
          "symbol": "AAPL",
          "last": 247.45,
          "change_24h_pct": -0.75,
          "volume_24h": 39698000,
          "source": "yfinance",
          "asof": "2025-10-20T11:35:02+00:00",
          "bars_used": 2,
          "checksum": "..."
        }
      ],
      "not_investment_advice": true
    }
  }
}

Timeseries

POST -> http://localhost:8000/mcp
Body

{
    "jsonrpc": "2.0",
    "id": 1,
    "method": "tools/call",
    "params": {
        "name": "timeseries",
        "arguments": {
            "input": {
                "symbol": "BTC/USDT",
                "interval": "1h",
                "limit": 120
            }
        }
    }
}

Response (truncated)

{
  "result": {
    "structuredContent": {
      "symbol": "BTC/USDT",
      "interval": "1h",
      "bars_used": 120,
      "source": "ccxt:binance",
      "bars": [
        {
          "timestamp": 1734703200000,
          "open": 103412.5,
          "high": 104011.0,
          "low": 103122.2,
          "close": 103982.4,
          "volume": 1845.1
        }
      ],
      "not_investment_advice": true
    }
  }
}

Analyze Asset

POST -> http://localhost:8000/
Body

{
    "jsonrpc": "2.0",
    "id": 1,
    "method": "tools/call",
    "params": {
        "name": "analyze_asset",
        "arguments": {
            "input": {
                "symbol": "ETH/USDT",
                "interval": "1d",
                "limit": 60,
                "mode": "full"
            }
        }
    }
}

Response (excerpt)

{
  "result": {
    "structuredContent": {
      "symbol": "ETH/USDT",
      "window_used_bars": 60,
      "trend_regime": "down",
      "window_return": -0.16509,
      "volatility_annualized": 0.55352,
      "sharpe": -1.114,
      "max_drawdown": -0.2246,
      "rsi_last": 45.63,
      "summary": "ETH/USDT (60bars 1d) regime: down. Window ret: -16.51%. Ann. vol: 55.35%, Sharpe: -1.11, Max DD: -22.46%. RSI(14): 45.6. | MACD hist: -18.84 | ATR(14)/P: 6.19% | %B: 0.35 | Sortino: -1.42 | VaR95 (hist): -5.65%. Signals are indicative only.",
      "indicators": {
        "ema20": 4114.65,
        "ema50": 4262.51,
        "macd": -108.56,
        "atr14": 249.59,
        "bb_percent": 0.35
      },
      "risk_extras": {
        "sortino": -1.425,
        "var_hist_95": -0.05649,
        "cvar_95": -0.0916
      },
      "source": "ccxt:binance",
      "asof": "2025-10-20T13:07:46+00:00",
      "meta": {
        "interval": "1d",
        "limit": 60,
        "units": {
          "window_return": "fraction",
          "volatility_annualized": "fraction",
          "max_drawdown": "fraction",
          "sharpe": "unitless"
        }
      },
      "not_investment_advice": true
    }
  }
}

Supported Intervals

1m, 3m, 5m, 15m, 30m, 1h, 2h, 4h, 6h, 8h, 12h, 1d, 3d, 1w, 1wk, 1M, 1mo

For equities (yfinance), only 1d, 1h, and 1wk are supported.

🔒 Provenance

Every result includes:

  • asof: ISO-8601 UTC timestamp
  • source: "ccxt:binance" or "yfinance"
  • checksum: SHA-256 hash of the serialized result -> ensures integrity and reproducibility.

⚠️ Disclaimer

This project is for educational and analytical purposes only. All responses include "not_investment_advice": true — do not use for live trading or financial decision-making.

Planned Features

  • add custom candles for indicators like RSI, MACD, ATR
  • compare_assets, portfolio_analyze, news_digest
  • Caching, LLM summaries, SQLite persistence
  • full OpenMCP compliance

Claude Desktop Configuration

Windows

{
  "mcpServers": {
    "finance-intel": {
      "command": "C:\\PATH_TO_PROJECT\\.venv\\Scripts\\fastmcp.exe",
      "args": [
        "run",
        "C:\\PATH_TO_PROJECT\\mcp-finance-intel\\server.py:mcp"
      ],
      "cwd": "C:\\PATH_TO_PROJECT\\mcp-finance-intel"
    }
  }
}

Linux

{
  "mcpServers": {
    "finance-intel": {
      "command": "/ABSOLUTE/PATH/TO/mcp-finance-intel/.venv/bin/fastmcp",
      "args": [
        "run",
        "server.py:mcp"
      ],
      "cwd": "/ABSOLUTE/PATH/TO/mcp-finance-intel"
    }
  }
}

🐳 Optional: Run in Docker

You can run the MCP server in a containerized environment:

FROM python:3.12-slim

WORKDIR /app
COPY . .
RUN pip install --no-cache-dir -r requirements.txt

EXPOSE 8000
CMD ["python", "server.py"]

Build & Run

docker build -t mcp-finance-intel .
docker run -p 8000:8000 mcp-finance-intel

☕ Support the Project

If you enjoy using mcp-finance-intel and want to help me keep building,
you can support me here:

👉 Buy Me a Coffee