ChessPal Mcp Engine

What is ChessPal Mcp Engine about?

ChessPal Mcp Engine exposes the world‑class Stockfish chess engine as an MCP server. It allows external applications to interact with the engine programmatically, requesting move calculations, validation, and game state information without dealing directly with the UCI protocol.

How to use ChessPal Mcp Engine?

1. Install the package from PyPI:

   pip install chesspal-mcp-engine
   

2. Configure the Stockfish binary via the CHESSPAL_ENGINE_PATH environment variable or the fallback variables (CHESSPAL_ENGINE_NAME, CHESSPAL_ENGINE_VERSION, etc.).
3. Start the server (default SSE transport):

   poetry run python -m chesspal_mcp_engine.main
   # or
   poetry run chesspal-mcp-engine
   

   For stdio transport:

   poetry run chesspal-mcp-engine --transport stdio
   

4. Call tools from a client using an MCP SSE or stdio client. Example (SSE):

   from mcp.client.sse import sse_client
   from mcp import ClientSession
   
   async def get_best_move():
       async with sse_client("http://127.0.0.1:9000/sse") as streams:
           async with ClientSession(*streams) as session:
               await session.initialize()
               result = await session.call_tool('get_best_move_tool', {
                   "request": {"fen": "rnbqkbnr/pppppppp/8/8/8/8/PPPPPPPP/RNBQKBNR w KQkq - 0 1", "move_history": []}
               })
               print(result.best_move_uci)
   

Key Features

• Robust integration with Stockfish, including process management and configurable depth/timeout.
• MCP server built with FastMCP, supporting both Server‑Sent Events (SSE) and stdio transports.
• Exposed tools: get_best_move_tool, validate_move_tool, get_legal_moves_tool, get_game_status_tool.
• Full support for FEN strings and optional move history.
• Comprehensive test suite (unit and integration) with TDD approach.
• Environment‑driven configuration for engine path, depth, timeout, host, and port.
• Graceful shutdown handling and detailed logging.

Use Cases

• Game AI bots that need a reliable engine backend for move generation.
• Educational tools teaching chess tactics by validating user moves.
• Web or desktop chess clients requiring a lightweight server to offload computation.
• Research projects that need to batch‑process large numbers of positions.
• Integration with Claude Desktop or other MCP‑compatible assistants for natural‑language chess interaction.

FAQ

Q: Do I need to build Stockfish myself? A: No. You can install a pre‑compiled binary via package managers, download from the official releases, or set CHESSPAL_ENGINE_PATH to point to any existing executable.

Q: Which transport should I choose? A: SSE is recommended for networked clients (HTTP). Use stdio for local integrations or when embedding the server in another process.

Q: How do I change the engine calculation depth? A: Set the CHESSPAL_ENGINE_DEPTH environment variable before starting the server.

Q: What if the engine crashes? A: The wrapper includes error handling and automatic restart logic; logs will indicate the failure.

Q: Can I run multiple engine instances? A: Yes, by launching the server on different ports or hosts with separate environment configurations.

ChessPal Chess Engine - A Stockfish-powered chess engine exposed as an MCP server using FastMCP

A Stockfish-powered chess engine exposed as an MCP server using FastMCP. Calculates best moves via MCP tools accessible over SSE (default) or stdio transports using an MCP client library. Part of the ChessPal project.

Features

• Robust Stockfish engine integration with proper process management
• Exposes engine functionality via the Model Context Protocol (MCP) using FastMCP.
• Supports both SSE and stdio MCP transports for client interaction.
• UCI protocol implementation for chess move generation
• Comprehensive test suite with TDD approach
• Error handling and recovery mechanisms
• Support for FEN positions and move history
• Flexible engine binary configuration

Prerequisites

• Python 3.10 or higher
• Poetry for dependency management (install from Poetry's documentation)
• Stockfish chess engine binary (version 17.1 recommended)

Installation

Install the published package from PyPI using pip:

pip install chesspal-mcp-engine

Installation for development

1. Clone the repository:

git clone https://github.com/wilson-urdaneta/dylangames-mcp-chess-engine.git
cd dylangames-mcp-chess-engine

2. Install dependencies and create virtual environment using Poetry:

poetry install

3. Configure the engine binary:
   • Option 1: Set CHESSPAL_ENGINE_PATH environment variable to point to your Stockfish binary
   • Option 2: Use the fallback configuration with these environment variables:

     # All variables have defaults, override as needed
     export CHESSPAL_ENGINE_NAME=stockfish     # Default: stockfish
     export CHESSPAL_ENGINE_VERSION=17.1       # Default: 17.1
     export CHESSPAL_ENGINE_OS=macos           # Default: automatically detected based on platform
     export CHESSPAL_ENGINE_BINARY=stockfish   # Default: stockfish (include .exe for Windows)
     

Stockfish Binary Setup

The ChessPal Chess Engine requires a Stockfish binary to run the server and integration tests. You have three options for setting up the binary:

Option 1: Set CHESSPAL_ENGINE_PATH (Recommended)

Point to any Stockfish executable on your system:

# Unix-like systems (binary from apt/brew or downloaded)
export CHESSPAL_ENGINE_PATH=/usr/local/bin/stockfish

# Windows (PowerShell)
$env:CHESSPAL_ENGINE_PATH="C:\path\to\stockfish.exe"

The binary can be:

• System-installed via package managers (apt install stockfish, brew install stockfish)
• Downloaded from Stockfish releases
• Manually compiled from source

Option 2: Use engines/ Directory

If CHESSPAL_ENGINE_PATH is not set, the server will look for the binary in a predefined directory structure:

1. Download the official binary for your OS from Stockfish releases
2. Place it in: engines/stockfish/<version>/<os>/<binary_name>
   • See engines/README.md for the exact directory structure
3. Set CHESSPAL_ENGINE_OS environment variable to match your system:

   export CHESSPAL_ENGINE_OS=macos    # For macOS
   export CHESSPAL_ENGINE_OS=linux    # For Linux
   export CHESSPAL_ENGINE_OS=windows  # For Windows
   

Option 3: Build from Source (Advanced)

Advanced users can compile Stockfish from source using our separate dylangames-engine repository. This option provides maximum control over the build configuration but requires C++ development experience.

Usage

Starting the Server

The server uses FastMCP with support for both Server-Sent Events (SSE) and stdio transports. You can start it using:

SSE Mode (Default)

poetry run python -m chesspal_mcp_engine.main
# Or
poetry run python -m chesspal_mcp_engine.main --transport sse

This command starts the MCP server in SSE mode, which listens for SSE connections on the configured host and port (default: 127.0.0.1:9000). This mode is ideal for programmatic clients and agents that need to interact with the chess engine over HTTP.

You can also use the entry point command:

poetry run chesspal-mcp-engine

Stdio Mode

poetry run python -m chesspal_mcp_engine.main --transport stdio
# Or
poetry run chesspal-mcp-engine --transport stdio

This command starts the MCP server in stdio mode, which communicates through standard input/output. This mode is useful for direct integration with tools like Claude Desktop or for testing purposes.

API Endpoints

The module exposes the following endpoints through FastMCP:

• get_best_move_tool: Get the best move for a given chess position
• validate_move_tool: Validate if a move is legal in a given position
• get_legal_moves_tool: Get all legal moves in a given position
• get_game_status_tool: Get the current game status (in progress, checkmate, etc.)

Example request using the MCP SSE client:

from mcp.client.sse import sse_client
from mcp import ClientSession

async def get_best_move():
    # Connect to the SSE endpoint
    async with sse_client("http://127.0.0.1:9000/sse", timeout=10.0) as streams:
        # Create an MCP session
        async with ClientSession(*streams) as session:
            # Initialize the session
            await session.initialize()

            # Call the tool - Note: Arguments MUST be wrapped in a "request" field
            result = await session.call_tool('get_best_move_tool', {
                "request": {  # Required wrapper field
                    "fen": "rnbqkbnr/pppppppp/8/8/8/8/PPPPPPPP/RNBQKBNR w KQkq - 0 1",
                    "move_history": []
                }
            })

            print(f"Best move: {result.best_move_uci}")  # e.g., "e2e4"

Request Format

The get_best_move_tool expects requests in the following format:

{
    "request": {
        "fen": "string",       // Required: FEN string representing the position
        "move_history": []     // Optional: List of previous moves in UCI format
    }
}

Note: The outer "request" wrapper field is required for proper request validation.

Timeouts

The engine is configured with the following timeouts:

• Engine calculation time: 1000ms by default (configurable via CHESSPAL_ENGINE_TIMEOUT_MS)
• Response wait timeout: 30s (allows time for engine initialization and calculation)
• SSE client connection timeout: 15s (configurable in client code)

These timeouts ensure reliable operation while allowing sufficient time for move calculation, even on slower systems or when the engine needs more time to process complex positions.

Environment Variables

The module uses the following environment variables for configuration:

# Primary configuration
CHESSPAL_ENGINE_PATH=/path/to/your/engine/binary

# Fallback configuration (used if CHESSPAL_ENGINE_PATH is not set/invalid)
CHESSPAL_ENGINE_NAME=stockfish       # Default: stockfish
CHESSPAL_ENGINE_VERSION=17.1         # Default: 17.1
CHESSPAL_ENGINE_OS=macos             # Default: auto-detected based on platform
CHESSPAL_ENGINE_BINARY=stockfish     # Default: stockfish (include .exe for Windows)

# Engine parameters
CHESSPAL_ENGINE_DEPTH=10             # Default: 10
CHESSPAL_ENGINE_TIMEOUT_MS=1000      # Default: 1000

# MCP Server Configuration
MCP_HOST=127.0.0.1                   # Default: 127.0.0.1
MCP_PORT=9000                        # Default: 9000

# Logging configuration
ENVIRONMENT=development              # Default: development
LOG_LEVEL=INFO                       # Default: INFO for production, DEBUG for development

See .env.example for a complete example configuration.

Development

Project Structure

dylangames-mcp-chess-engine/
├── src/                    # Source code
│   └── chesspal_mcp_engine/
│       ├── __init__.py
│       ├── main.py        # FastMCP server
│       ├── engine_wrapper.py  # Stockfish wrapper
│       ├── config.py      # Configuration management
│       ├── logging_config.py # Logging setup
│       ├── shutdown.py    # Graceful shutdown handling
│       └── models.py      # Data models
├── tests/                 # Test suite
│   └── test_engine_wrapper.py
├── engines/              # Engine binaries directory
├── pyproject.toml       # Poetry dependencies and configuration
├── poetry.lock         # Locked dependencies
├── .env.example        # Environment variables example
└── README.md          # This file

Development Workflow

1. Install dependencies:

poetry install

2. Activate the virtual environment:

poetry shell

3. Run tests:

poetry run pytest
poetry run pytest tests/ -v

4. Run code quality tools:

poetry run black .
poetry run isort .
poetry run flake8
poetry run pre-commit run --all-files

5. Using the mcp inspector:

poetry run mcp dev src/chesspal_mcp_engine/main.py

# In the inspector UI
# STDIO configuration
Command: poetry
Arguments: run python -m chesspal_mcp_engine.main --transport stdio

# SSE
# In a separate terminal run the app in SSE mode
poetry run python -m chesspal_mcp_engine.main
# In the mcp inspector UI
Transport Type > SSE

{
  "fen": "r3k2r/p1ppqpb1/bn2pnp1/3PN3/1p2P3/2N2Q1p/PPPBBPPP/R3K2R w KQkq - 0 1",
  "move_history": []
}

Adding Dependencies

To add new dependencies:

# Add a production dependency
poetry add package-name

# Add a development dependency
poetry add --group dev package-name

Code Quality

The codebase follows these standards:

• Type hints for all functions
• Comprehensive error handling
• Detailed docstrings (Google style)
• PEP 8 compliance via Black, isort, and flake8
• Proper resource management

Contributing

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Run tests and linters:

   poetry run black .
   poetry run isort .
   poetry run flake8
   poetry run pytest
   

5. Submit a pull request

CI/CD Pipeline

The project uses GitHub Actions for continuous integration and deployment. The pipeline is triggered on:

• Push to main branch
• Pull requests to main branch
• Tag pushes starting with v (e.g., v1.0.0)

Pipeline Stages

1. Lint (lint job)

   • Runs on Ubuntu latest
   • Checks code formatting with Black
   • Verifies import sorting with isort
   • Performs code quality checks with flake8

2. Test (test job)

   • Runs on Ubuntu latest
   • Installs Stockfish engine
   • Executes the test suite with pytest

3. Package (package job)

   • Runs after successful lint and test
   • Builds the Python package using Poetry
   • Uploads build artifacts for release

4. Release (release job)

   • Runs only on version tags (e.g., v1.0.0)
   • Creates GitHub releases
   • Optionally publishes to PyPI (disabled by default)

Versioning and Tags

The project uses semantic versioning with two types of tags:

1. External Releases (e.g., v1.0.0)

   • Public releases available to users
   • Triggers full release process
   • Creates GitHub release with release notes
   • Can optionally publish to PyPI

2. Internal Releases (e.g., v1.0.0-internal)

   • Used for internal testing and development
   • Skips the release job
   • Useful for testing release process without affecting public releases

PyPI Publishing

PyPI publishing is disabled by default. To enable:

1. Set ENABLE_PYPI to true in the workflow file
2. Configure PYPI_TOKEN secret in GitHub repository settings

License

GNU General Public License v3.0 - see LICENSE file for details.

Support

For issues and feature requests, please use the GitHub issue tracker.

Running Tests

The project includes both unit tests and integration tests:

Running All Tests

poetry run pytest

This runs the complete test suite. Note that integration tests require a Stockfish binary to be available through either Option 1 or 2 above.

Running Unit Tests Only

poetry run pytest -m "not integration"

This runs only the unit tests, where Stockfish interaction is mocked and no binary is required.

Test Categories

• Unit Tests: Test individual components with mocked dependencies
• Integration Tests: Test actual Stockfish binary interaction
  • Require Stockfish binary (see setup options above)
  • Test real engine initialization and move calculation
  • Skip automatically if no binary is available