Time Series Quant Finance MCP Server

An MCP (Model Context Protocol) server built using FastMCP that calculates technical analysis indicators for financial stock tickers using yfinance and pandas-ta.

This server provides LLMs with access to real-time financial market data and quantitative analysis tools, enabling them to analyze stock trends, momentum, volatility, and key moving averages.

Features

• Automatic Baseline Trends: Always includes 50-day Simple Moving Average (SMA) and 200-day SMA to establish baseline trends.

• Multiple Technical Indicators: Support for:

  • RSI (Relative Strength Index)

  • MACD (Moving Average Convergence Divergence)

  • BBANDS (Bollinger Bands)

  • ATR (Average True Range)

  • SMA (Simple Moving Average with custom lengths)

  • EMA (Exponential Moving Average with custom lengths)

• Robust Multi-index Handling: Programmatically flattens yfinance multi-level indices to prevent Pandas crashes.

• JSON Output: Returns the last 5 trading days of historical and calculated data, cleaned and formatted for easy consumption by an LLM.

Related MCP server: trade-mcp

Installation & Setup

Prerequisites

• Python >= 3.12

• uv (recommended package manager) or standard pip

1. Clone & Install Dependencies

Using uv (recommended):

# Install dependencies using uv
uv sync

Or using standard pip and virtual environments:

# Create and activate a virtual environment
python3 -m venv .venv
source .venv/bin/activate

# Install dependencies
pip install -r requirements.txt

Running and Debugging

Running via FastMCP Dev Inspector

FastMCP includes a built-in development inspector that lets you interact with and test the server in a web UI.

# Using uv
uv run fastmcp dev server.py

# Or using standard python
python server.py dev

Alternatively, you can use the official MCP Inspector:

npx -y @modelcontextprotocol/inspector uv run server.py

MCP Configuration

To integrate this server with client applications like Claude Desktop, add the configuration to your MCP settings file (typically ~/Library/Application Support/Claude/claude_desktop_config.json on macOS or %APPDATA%\Claude\claude_desktop_config.json on Windows).

Configuration using uv (Recommended)

{
  "mcpServers": {
    "finance-server": {
      "command": "uv",
      "args": [
        "run",
        "--directory",
        "/absolute/path/to/mcp-finance-server",
        "server.py"
      ]
    }
  }
}

Configuration using Virtual Environment Python

{
  "mcpServers": {
    "finance-server": {
      "command": "/absolute/path/to/mcp-finance-server/.venv/bin/python",
      "args": [
        "/absolute/path/to/mcp-finance-server/server.py"
      ]
    }
  }
}

Make sure to replace /absolute/path/to/mcp-finance-server with the actual path to the repository on your system.

Tools Reference

calculate_technical_indicators

Fetches daily historical stock data for the last year and calculates requested technical indicators.

Parameters

Return Value

Returns a JSON-formatted string mapping date strings to indicator values for the last 5 trading days.

Example return structure (shortened for readability):

{
  "2026-07-07": {
    "Open": 182.5,
    "High": 184.2,
    "Low": 181.8,
    "Close": 183.9,
    "Volume": 54200000,
    "Baseline_SMA_50": 178.4,
    "Primary_Trend_SMA_200": 170.2,
    "RSI_14": 62.4
  },
  "2026-07-08": { ... },
  "2026-07-09": { ... },
  "2026-07-10": { ... },
  "2026-07-11": { ... }
}