Case Study

Building ChessMCP ChatGPT App Store

Model Context Protocol chess assistant with Stockfish

Stockfish 16

Engine

ChatGPT + MCP

Interface

Web widget

Visualization

Project Overview

The Challenge

Chat-based chess play and analysis inside ChatGPT lacked a production-grade chess engine, visual board, and reliable move analysis.

The Solution

Built a Model Context Protocol (MCP) server that connects ChatGPT to Stockfish with play, best-move, and position-analysis capabilities plus a web widget for board visualization.

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Key Impact

1Delivers real-time move recommendations from Stockfish inside ChatGPT
2Enables full game play with stateful server-side board tracking
3Provides sharable board widget for human-friendly visualization

Technical Approach

How We Built It

Model Context Protocol Integration

Built using Anthropic's Model Context Protocol (MCP), ChessMCP creates a bridge between ChatGPT and Stockfish, enabling seamless chess interactions:

•MCP Server: Python-based server implementing the MCP specification for tool discovery and execution
•Stockfish Integration: Direct UCI (Universal Chess Interface) communication with Stockfish 16 for move analysis
•Stateful Game Management: Server-side board state tracking for continuous game play across conversations
•Web Visualization Widget: Shareable board visualization with move history and position display

Key Features

•Play full chess games with ChatGPT as your opponent or coach
•Get best move recommendations at any position
•Analyze positions with engine evaluation scores
•Visual board representation for easy understanding
•Move validation and legal move suggestions

Challenges & Solutions

Overcoming Obstacles

Stateful Conversations

Maintaining game state across multiple chat messages required careful session management and board state persistence in the MCP server.

UCI Protocol Integration

Integrating Stockfish's UCI protocol with MCP's tool-based architecture required building a reliable communication layer that handles engine responses and timeouts.

User Experience

Chess positions are complex to describe in text. The web widget solution provides visual clarity while keeping the core interaction conversational.

Technical Scope

Full-Stack AI Platform

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MCP Server

Protocol Implementation

Python 3.11MCP SDKFastAPIWebSocketsAsync I/O

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Chess Engine

Stockfish 16

Stockfish 16UCI ProtocolPosition AnalysisMove GenerationEvaluation

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Frontend

Board Visualization

ReactChess.jsChessboard.jsxTailwind CSSVercel

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Integration

ChatGPT App Store

OpenAI APIMCP ProtocolTool CallingSession Management

Impact & Results

ChessMCP demonstrates the power of MCP for building specialized AI assistants:

•Production-Ready: Fully functional chess assistant available in ChatGPT App Store
•Professional Analysis: Stockfish 16 provides grandmaster-level move recommendations
•Seamless UX: Natural language chess play without leaving ChatGPT
•Open Source: Available as a reference implementation for MCP developers
•Extensible: Architecture can be adapted for other game engines and specialized tools

The project serves as a blueprint for developers building MCP-based tools, showing how to combine powerful external engines with conversational AI interfaces.

“

ChessMCP shows how MCP can bring specialized expertise into ChatGPT. The integration is seamless, and having Stockfish analysis in my conversations is incredibly useful for studying chess.

Chess Enthusiast

Early Adopter