MCP-to-MCP Tic-Tac-Toe
Enables two LLMs to play Tic-Tac-Toe against each other autonomously using a shared tool and an SSE relay. The server facilitates agent-to-agent communication by holding tool responses until the opponent makes a move, managing the game state in real-time.
README
MCP-to-MCP Communication
Two LLMs play Tic-Tac-Toe against each other through a single MCP tool make_move. No human input, AIs autonomously take turns via ping-pong SSE relay.
Play
Local (Claude Code, Codex) Or Remote
# (starts local server on :8787 via stdio, or auto-joins if another instance is already hosting)
claude mcp add tictactoe -- npx -y github:PsychoSmiley/mcp-to-mcp
# Or using Cloudflare MCP Remote
claude mcp add tictactoe --transport http https://mcp-tictactoe.edge-relay-9x.workers.dev/mcp
Or simply from claude.ai web in Settings -> Connectors URL: https://mcp-tictactoe.edge-relay-9x.workers.dev/mcp
# Optionally, to self-host on Cloudflare Workers (free)
Set CLOUDFLARE_API_TOKEN=your-token-here && Set CLOUDFLARE_ACCOUNT_ID=your-account-id && git clone https://github.com/PsychoSmiley/mcp-to-mcp && cd mcp-to-mcp && npm install && npx wrangler deploy # Auto-deploys on push via GitHub Actions (add secrets in repo Settings -> Secrets).
Then open two separate Claude chats. In each ask: use make_move to play tic-tac-toe
Why you should care
The idea isn't Tic-Tac-Toe - it's the architecture. Using MCP itself as a ping-pong turn relay, where each tool call is the AI's response back-and-forth like a baton relay. This achieves synchronous P2P agent communication over a stateless REST protocol, with zero database reads/writes. The same pattern could be used for chat between two or more AI agents via MCP ;)
How it works
Each make_move(move) call both submits a move and waits for the opponent's reply - send + block + receive in one MCP call. The server is authoritative: LLMs only send moves, never the board state. No database - game state lives in RAM. Local MCP Session-Id identifies each player (X vs O).
-> time -> Each MCP closes only to receive opponent's move result, then immediately answers
Agent A: <mcp make_move("B2")>{LLM think}<mcp make_move("C3")>{LLM think}<mcp make_move("B1")>
Agent B: <mcp make_move("A1")>{LLM think}<mcp make_move("C1")>{LLM think}
-> "Game over - X wins!"
server.js- Game logic + local Node.js server (stdio + HTTP)worker.js- Cloudflare Worker + Durable Object (imports game logic from server.js)
Why Durable Object
Locally (server.js), the Node.js process is that shared room. Remotely (worker.js), the Durable Object is.
A Cloudflare Worker is stateless - each request spawns a fresh isolate that dies after responding. Two players' requests can land on different isolates in different cities with no shared memory. They can't meet, can't pass state, can't even know each other exist. Cloudflare KV could bridge them (shared key-value store), but the free tier limits writes to 1,000/day - tight for a game with many moves.
A Durable Object is basically a tiny managed server (persistent single-threaded process with RAM). All requests route to the same instance via idFromName("lobby") - both players share one this.game variable. When Player B places a move, Player A's polling loop (yielding via setTimeout) sees the change on the next tick. No database, no transfers, no serialization - just two requests reading the same variable on the same event loop. (Durable Object SQLite could also replace KV with unlimited read/write, but RAM is faster for short-lived games.)
Recommended Servers
playwright-mcp
A Model Context Protocol server that enables LLMs to interact with web pages through structured accessibility snapshots without requiring vision models or screenshots.
Audiense Insights MCP Server
Enables interaction with Audiense Insights accounts via the Model Context Protocol, facilitating the extraction and analysis of marketing insights and audience data including demographics, behavior, and influencer engagement.
Magic Component Platform (MCP)
An AI-powered tool that generates modern UI components from natural language descriptions, integrating with popular IDEs to streamline UI development workflow.
VeyraX MCP
Single MCP tool to connect all your favorite tools: Gmail, Calendar and 40 more.
Kagi MCP Server
An MCP server that integrates Kagi search capabilities with Claude AI, enabling Claude to perform real-time web searches when answering questions that require up-to-date information.
graphlit-mcp-server
The Model Context Protocol (MCP) Server enables integration between MCP clients and the Graphlit service. Ingest anything from Slack to Gmail to podcast feeds, in addition to web crawling, into a Graphlit project - and then retrieve relevant contents from the MCP client.
E2B
Using MCP to run code via e2b.
Neon Database
MCP server for interacting with Neon Management API and databases
Exa Search
A Model Context Protocol (MCP) server lets AI assistants like Claude use the Exa AI Search API for web searches. This setup allows AI models to get real-time web information in a safe and controlled way.
Qdrant Server
This repository is an example of how to create a MCP server for Qdrant, a vector search engine.