Chess MCP

The Motivation

This project started with a simple, practical question: "How does the Model Context Protocol (MCP) actually work?" The easiest explanation I could come up with was to describe it as an API for LLMs, but showing is always better than telling.

To make this abstract concept tangible, I built Chess MCP—a project that serves as both a fully functional chess engine and a clear case study for extending the capabilities of large language models.

The Project

Chess MCP is a powerful, standalone chess engine built in TypeScript. It allows users to play chess against a sophisticated AI, analyze positions, or even pit two AIs against each other.

The core innovation is its ability to act as a specialized "tool" that any MCP-compatible AI assistant (like Anthropic's Claude or Raycast) can connect to. The LLM doesn't need to know the rules of chess; it only needs to know how to talk to my engine.

How It Works

The key architectural principle is a clean separation of concerns:

The LLM (The Conversationalist): The AI assistant's role is simply to manage the user interaction. It processes natural language requests like "Start a new game" or "Move my pawn to e4."

The MCP (The Bridge): The Model Context Protocol acts as the API contract between the LLM and the engine. The LLM can see the list of available "tools" (e.g., ⁠make_move, ⁠analyze_position) and decides which one to use based on the user's prompt.
The Chess Engine (The Specialist): My custom-built engine does all the heavy lifting. It enforces chess rules, tracks the game state, and contains the AI logic for the opponent. It receives a command from the LLM, executes it, and returns a structured result.

This design pattern demonstrates a powerful future for AI applications, where generalist language models can be augmented with highly specialized, reliable, and independently-maintained tools.

Key Features & Highlights

Advanced Chess AI: A challenging opponent built with a Minimax algorithm, alpha-beta pruning, and multiple difficulty levels.
Performance Optimization: The engine is optimized for speed and efficiency using transposition tables (a smart caching strategy to avoid re-calculating known positions) and iterative deepening for better time management during analysis.
Full MCP Integration: Provides a comprehensive set of tools, allowing an LLM to manage game state, make moves, and request analysis seamlessly.
Standalone CLI Mode: In addition to MCP integration, the project includes a full-featured interactive command-line interface for local play and testing.

Technical Stack

Language: TypeScript
Runtime: Node.js
Core Logic: chess.js
Protocol: Model Context Protocol (MCP)
Execution: tsx for direct TypeScript execution

Outcome

This project successfully demonstrates a practical application of the Model Context Protocol, providing a clear and functional example of how to delegate complex, domain-specific tasks from an LLM to an external tool. It serves as an excellent educational resource for developers interested in the future of AI integration and agentic workflows.