MCP Financial Analyst

A local MCP (Model Context Protocol) server that acts as a financial analyst: it fetches real market data from Yahoo Finance, runs lightweight analysis (moving averages, returns, volatility), and generates price charts. No hallucinated numbers—all data comes from yfinance.

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Quick start (how to use the app)

1. Create a venv and install dependencies

Use a virtual environment so you avoid conda/base Python and Homebrew’s “externally managed” pip. Use Python 3.12+ (MCP currently supports 3.10–3.13; 3.14 support may lag).

cd MCP_StockTool
python3.12 -m venv .venv  # or: python -m venv .venv
.venv/bin/pip install -r requirements.txt

If you don't have Python 3.12, install it with your package manager (e.g. Homebrew on macOS), then run the two commands above with that Python.

2. Add the MCP server to MCP Client

Link this MCP Server to an existing MCP Client + LLM platforms like GitHub Copilot, Cursor or Claude Desktop to use: ** Repace /absolute/path/to with location of this server on your local computer.

{
  "mcpServers": {
    "financial-analyst": {
      "command": "python",
      "args": ["server.py"],
      "cwd": "/absolute/path/to/MCP_StockTool"
    }
  }
}

• Save. Restart Cursor (or reload the window) so it picks up the server.

3. Use it in chat

In the Platform / IDE of your choice, open the chat, ask things like:

• "Fetch the last 3 months of daily data for AAPL."
• "Analyze MSFT from 2026-01-01 to 2026-02-01 and show me the chart and key metrics."
• "What’s the 30-day moving average and return for GOOGL over the last 6 months?"

The AI will call the financial tools, get real data, and answer with numbers and (when you use analyze) a chart path. Charts are saved under MCP_StockTool/charts/.

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1. fetch_market_data – Get OHLCV data for a ticker over a date range.
2. analyze_and_chart – Get the same data, compute 7-day/30-day MAs, return %, and volatility, and save a price chart.

Requirements

• Python 3.10+
• Dependencies: mcp, yfinance, pandas, matplotlib

Install

From the project root:

cd MCP_StockTool
pip install -r requirements.txt

Or with uv:

uv pip install -r requirements.txt

Run the server (stdio)

For use by an MCP client (Cursor, Claude Desktop, etc.):

python server.py

The server uses stdio transport: the client spawns this process and talks over stdin/stdout. Do not run it interactively; the client will start it.

MCP Client Integration

Add this MCP server in Github Copilot/Cursor/Claude Desktop so the AI can use the financial tools.

2. Add a server entry like this (adjust path if your project lives elsewhere):

{
  "mcpServers": {
    "financial-analyst": {
      "command": "python",
      "args": ["server.py"],
      "cwd": "/absolute/path/to/MCP_StockTool",
      "env": {}
    }
  }
}

If you use a virtualenv or uv:

{
  "mcpServers": {
    "financial-analyst": {
      "command": "/path/to/venv/bin/python",
      "args": ["server.py"],
      "cwd": "/absolute/path/to/MCP_StockTool"
    }
  }
}

Or with uv:

{
  "mcpServers": {
    "financial-analyst": {
      "command": "uv",
      "args": ["run", "python", "server.py"],
      "cwd": "/absolute/path/to/MCP_StockTool"
    }
  }
}

Restart App (or reload MCP) so it picks up the server. The AI will then see fetch_market_data and analyze_and_chart as available tools.

Tools

fetch_market_data

• ticker (str): Symbol, e.g. AAPL, MSFT.
• start_date (str): Start date YYYY-MM-DD.
• end_date (str): End date YYYY-MM-DD.
• interval (str): "daily" | "weekly" | "monthly" (default "daily").

Returns a JSON object with ticker, start_date, end_date, interval, row_count, and data (list of OHLCV rows). On error, returns an error field and empty or partial data.

analyze_and_chart

• ticker (str): Symbol, e.g. AAPL, MSFT.
• start_date (str): Start date YYYY-MM-DD.
• end_date (str): End date YYYY-MM-DD.
• interval (str): "daily" | "weekly" | "monthly" (default "daily").
• output_path (str, optional): Path for the chart image. If omitted, saves to charts/<ticker>_<timestamp>.png.

Returns a JSON object with:

• chart_path: Absolute path to the saved chart.
• metrics: ma_7d, ma_30d (when enough data), return_pct, volatility_annual_pct, data_points, first_close, last_close.
• ticker, start_date, end_date, interval.

Volatility is the annualized standard deviation of daily (or period) returns in percent.

Example prompts (for the AI using this server)

• “Fetch the last 3 months of daily data for AAPL.”
• “Analyze MSFT from 2024-01-01 to 2024-12-01 and show me the chart and key metrics.”
• “What’s the 30-day moving average and return for GOOGL over the last 6 months?”

The AI will call the MCP tools with the right parameters and report back using the real data and chart path returned by the server.

Project layout

MCP_StockTool/
├── pyproject.toml
├── README.md
├── server.py              # MCP entrypoint (stdio)
├── tools/
│   ├── __init__.py
│   ├── market_data.py     # fetch_market_data implementation
│   └── chart_analyzer.py  # analyze_and_chart implementation
└── charts/                # created at runtime for saved images

License

Use and modify as you like. Data is from Yahoo Finance via yfinance.