ChessPal MCP Server

What does “ChessPal” MCP Server do?

The ChessPal MCP Server is a specialized Model Context Protocol (MCP) server that exposes the capabilities of the Stockfish chess engine to AI assistants and clients. By acting as a bridge, ChessPal allows AI models and applications to access advanced chess functionalities programmatically. This enables tasks such as evaluating chess positions, generating move suggestions, analyzing games, and more—all through a standardized MCP interface. The server enhances developer workflows by allowing seamless integration of Stockfish’s powerful chess analysis into AI-driven tools, bots, or educational platforms, making it easier to build applications that require chess expertise without directly managing the complexities of the engine itself.

List of Prompts

No information about prompt templates is available in the repository.

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List of Resources

No explicit resources are described in the repository.

List of Tools

• Information about specific tools is not directly listed in the root documentation or visible code structure. Based on the context, the server exposes Stockfish chess engine functionalities via MCP, but exact tool names or methods are not specified.

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Use Cases of this MCP Server

• Chess Game Analysis
  Integrate Stockfish-based analysis into chess applications, allowing users to evaluate their games, get suggestions, and understand mistakes.
• AI Chess Bots
  Build AI-powered chess bots that can play against users or simulate games by leveraging Stockfish’s move generation and evaluation tools.
• Educational Chess Platforms
  Power chess learning tools that provide move-by-move feedback and tactical insights using the engine’s capabilities.
• Automated Game Annotation
  Generate annotated chess games, including best-move suggestions and blunder detection, for use in publications or chess communities.
• Integration into LLM Workflows
  Enhance AI assistants or chatbots with real-time chess advice and position analysis for interactive experiences.

How to set it up

Windsurf

1. Ensure prerequisites like Node.js and Docker are installed.
2. Locate your Windsurf configuration file.
3. Add the ChessPal MCP Server to your mcpServers object using a JSON snippet.
4. Save the configuration and restart Windsurf.
5. Verify setup by checking connectivity to the MCP server.

{
  "mcpServers": {
    "chesspal-mcp": {
      "command": "chesspal-mcp-engine",
      "args": []
    }
  }
}

Claude

1. Install required dependencies (e.g., Node.js).
2. Open Claude’s configuration file.
3. Insert the ChessPal MCP Server entry.
4. Save changes and restart Claude.
5. Confirm the MCP server is available in the interface.

{
  "mcpServers": {
    "chesspal-mcp": {
      "command": "chesspal-mcp-engine",
      "args": []
    }
  }
}

Cursor

1. Prepare your environment (Node.js, Docker).
2. Edit Cursor’s configuration file.
3. Add ChessPal MCP Server configuration as shown below.
4. Restart Cursor.
5. Validate that ChessPal MCP appears in your server list.

{
  "mcpServers": {
    "chesspal-mcp": {
      "command": "chesspal-mcp-engine",
      "args": []
    }
  }
}

Cline

1. Make sure all system requirements (such as Node.js) are met.
2. Go to your Cline configuration file.
3. Register the ChessPal MCP Server.
4. Save and restart Cline.
5. Test the connection to ensure proper setup.

{
  "mcpServers": {
    "chesspal-mcp": {
      "command": "chesspal-mcp-engine",
      "args": []
    }
  }
}

Securing API Keys

If the server requires API keys or secrets, store them in environment variables. Example:

{
  "mcpServers": {
    "chesspal-mcp": {
      "command": "chesspal-mcp-engine",
      "args": [],
      "env": {
        "MY_API_KEY": "${MY_API_KEY}"
      },
      "inputs": {
        "api_key": "${MY_API_KEY}"
      }
    }
  }
}

How to use this MCP inside flows

Using MCP in FlowHunt

To integrate MCP servers into your FlowHunt workflow, start by adding the MCP component to your flow and connecting it to your AI agent:

Click on the MCP component to open the configuration panel. In the system MCP configuration section, insert your MCP server details using this JSON format:

{
  "chesspal-mcp": {
    "transport": "streamable_http",
    "url": "https://yourmcpserver.example/pathtothemcp/url"
  }
}

Once configured, the AI agent is now able to use this MCP as a tool with access to all its functions and capabilities. Remember to change “chesspal-mcp” to the actual name of your MCP server and replace the URL with your own MCP server URL.

Overview

Between the available documentation and what is visible in the repository, ChessPal MCP Server appears to be a functional, single-purpose MCP server with clear setup guidance but lacks explicit documentation about resources, prompts, and tools. It does have a license (GPL-3.0), and is open source with minimal community activity. Based on these tables, I’d rate its completeness and documentation at 3/10.

MCP Score