MCP Trader Server

A Model Context Protocol (MCP) server for stock traders.

Features

Tools

The server provides the following tools for stock analysis and trading:

• analyze-stock: Performs technical analysis on a given stock symbol

  • Required argument: symbol (string, e.g. "NVDA")

  • Returns comprehensive technical analysis including:

    • Moving average trends (20, 50, 200 SMA)

    • Momentum indicators (RSI, MACD)

    • Volatility metrics (ATR, ADRP)

    • Volume analysis

• relative-strength: Calculates a stock's relative strength compared to a benchmark

  • Required argument: symbol (string, e.g. "AAPL")

  • Optional argument: benchmark (string, default: "SPY")

  • Returns relative strength metrics across multiple timeframes (21, 63, 126, 252 days)

  • Includes performance comparison between the stock and benchmark

• volume-profile: Analyzes volume distribution by price

  • Required argument: symbol (string, e.g. "MSFT")

  • Optional argument: lookback_days (integer, default: 60)

  • Returns volume profile analysis including:

    • Point of Control (POC) - price level with highest volume

    • Value Area (70% of volume range)

    • Top volume price levels

• detect-patterns: Identifies chart patterns in price data

  • Required argument: symbol (string, e.g. "AMZN")

  • Returns detected chart patterns with confidence levels and price targets

• position-size: Calculates optimal position size based on risk parameters

  • Required arguments:

    • symbol (string, e.g. "TSLA")

    • stop_price (number)

    • risk_amount (number)

    • account_size (number)

  • Optional argument: price (number, default: current price)

  • Returns recommended position size, dollar risk, and potential profit targets

• suggest-stops: Suggests stop loss levels based on technical analysis

  • Required argument: symbol (string, e.g. "META")

  • Returns multiple stop loss suggestions based on:

    • ATR-based stops (1x, 2x, 3x ATR)

    • Percentage-based stops (2%, 5%, 8%)

    • Technical levels (moving averages, recent swing lows)

Technical Analysis Capabilities

The server leverages several specialized analysis modules:

• TechnicalAnalysis: Core technical indicators and trend analysis

  • Moving averages (SMA 20, 50, 200)

  • Momentum indicators (RSI, MACD)

  • Volatility metrics (ATR, Average Daily Range Percentage)

  • Volume analysis (20-day average volume)

• RelativeStrength: Comparative performance analysis

  • Multi-timeframe relative strength scoring (21, 63, 126, 252 days)

  • Performance comparison against benchmark indices

  • Outperformance/underperformance classification

• VolumeProfile: Advanced volume analysis

  • Price level volume distribution

  • Point of Control (POC) identification

  • Value Area calculation (70% of volume)

• PatternRecognition: Chart pattern detection

  • Support/resistance levels

  • Common chart patterns (head and shoulders, double tops/bottoms, etc.)

  • Confidence scoring for detected patterns

• RiskAnalysis: Position sizing and risk management

  • Risk-based position sizing

  • Multiple stop loss strategies

  • R-multiple profit target calculation

Data Sources

The server uses the Tiingo API for market data:

• Historical daily OHLCV data

• Adjusted prices for accurate backtesting

• Up to 1 year of historical data by default

Related MCP server: Trading Simulator MCP Server

Setup

Prerequisites

• Python 3.11+

• uv

• ta-lib

• Tiingo API Key

Environment Variables

Create a .env file:

Installing via Smithery

To install Trader for Claude Desktop automatically via Smithery:

This will:

1. Install the MCP Trader server

2. Configure it with your Tiingo API key

3. Set up the Claude Desktop integration

Smithery Configuration

The server includes a smithery.yaml configuration file that defines:

• Required configuration parameters (Tiingo API key)

• Command function to start the MCP server

• Integration with Claude Desktop

You can customize the Smithery configuration by editing the smithery.yaml file.

Installation

Docker Deployment

The project includes a Dockerfile for containerized deployment:

To run the container in HTTP server mode:

Configuration

Claude Desktop App

On MacOS: ~/Library/Application\ Support/Claude/claude_desktop_config.json

On Windows: %APPDATA%/Claude/claude_desktop_config.json

Development Configuration:

Development

Build and Run

HTTP Server Mode

The server can also run as a standalone HTTP server for testing or integration with other applications:

This starts an HTTP server on http://localhost:8000 with the following endpoints:

• GET /list-tools: Returns a list of available tools and their schemas

• POST /call-tool: Executes a tool with the provided arguments

  • Request body format:

    { "name": "analyze-stock", "arguments": { "symbol": "AAPL" } }

  • Returns an array of content items (text, images, etc.)

Debugging

Use the MCP Inspector for debugging:

Example Usage

In Claude Desktop:

The server will return a technical analysis summary including trend status, momentum indicators, and key metrics.

Dependencies

See pyproject.toml for full dependency list:

Contributing

Contributions to MCP Trader are welcome! Here are some ways you can contribute:

• Add new tools: Implement additional technical analysis tools or trading strategies

• Improve existing tools: Enhance the accuracy or performance of current tools

• Add data sources: Integrate additional market data providers

• Documentation: Improve the documentation or add examples

• Bug fixes: Fix issues or improve error handling

Development Workflow

1. Fork the repository

2. Create a feature branch (git checkout -b feature/amazing-feature)

3. Commit your changes (git commit -m 'Add some amazing feature')

4. Push to the branch (git push origin feature/amazing-feature)

5. Open a Pull Request

Future Plans

The MCP Trader project has several planned enhancements:

• Portfolio Analysis: Tools for analyzing and optimizing portfolios

• Backtesting: Capabilities to test trading strategies on historical data

• Sentiment Analysis: Integration with news and social media sentiment data

• Options Analysis: Tools for analyzing options strategies and pricing

• Real-time Data: Support for real-time market data feeds

• Custom Strategies: Framework for implementing and testing custom trading strategies

• Alerts: Notification system for price and technical indicator alerts

Further Reading

Learn more about this project through these detailed blog posts:

• Building a Stock Analysis Server with MCP, Part 1 - Initial setup, architecture, and core technical analysis features

• Building a Stock Analysis Server with MCP, Part 2 - Relative Strength, Volume, Pattern Recognition, Risk analysis