ChessPal Chess Engine – A Stockfish‑Powered MCP Server

ChessPal turns the powerful Stockfish chess engine into a first‑class Model Context Protocol (MCP) service. By exposing UCI functionality over the MCP interface, developers can embed high‑quality chess analysis directly into AI assistants or any system that understands the MCP contract. The server is built on FastMCP, ensuring low‑latency communication over either Server‑Sent Events (SSE) or standard I/O streams, and it comes with a robust process manager that keeps the Stockfish binary alive and responsive.

The core problem this MCP solves is the lack of a lightweight, protocol‑agnostic chess engine that can be called from an AI assistant’s context. Traditional UCI integrations require custom adapters and are often coupled to a specific framework. ChessPal abstracts those details behind the MCP, letting an assistant request moves, evaluate positions, or generate move histories with a simple JSON payload. The result is a plug‑and‑play component that can be deployed behind any MCP client, whether it’s a local script or a cloud‑based LLM that supports tool calls.

Key features include:

• UCI compliance: Full support for Stockfish’s Universal Chess Interface, allowing move generation, position evaluation, and engine configuration.
• Transport flexibility: Operates over SSE for real‑time streaming or stdio for synchronous use, giving developers the right tool for their workflow.
• Process resilience: The server monitors the Stockfish process, automatically restarting it on crash or unresponsiveness, and provides graceful shutdown hooks.
• Position handling: Accepts FEN strings and move histories, enabling context‑aware analysis that can be chained with other MCP tools.
• Extensible configuration: Environment variables let you point to any Stockfish binary, adjust engine parameters, or switch between versions without code changes.

Typical use cases span a wide spectrum: an AI tutor that explains chess tactics, a game‑analysis bot that evaluates user games in real time, or a LLM that generates annotated puzzles. Because the engine is exposed via MCP, any assistant that supports tool calls can treat chess evaluation as a first‑class capability—no custom engine integration required. The server’s clean, test‑driven design ensures reliability in production environments and easy maintenance for developers.