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.)
推荐服务器
Baidu Map
百度地图核心API现已全面兼容MCP协议,是国内首家兼容MCP协议的地图服务商。
Playwright MCP Server
一个模型上下文协议服务器,它使大型语言模型能够通过结构化的可访问性快照与网页进行交互,而无需视觉模型或屏幕截图。
Magic Component Platform (MCP)
一个由人工智能驱动的工具,可以从自然语言描述生成现代化的用户界面组件,并与流行的集成开发环境(IDE)集成,从而简化用户界面开发流程。
Audiense Insights MCP Server
通过模型上下文协议启用与 Audiense Insights 账户的交互,从而促进营销洞察和受众数据的提取和分析,包括人口统计信息、行为和影响者互动。
VeyraX
一个单一的 MCP 工具,连接你所有喜爱的工具:Gmail、日历以及其他 40 多个工具。
graphlit-mcp-server
模型上下文协议 (MCP) 服务器实现了 MCP 客户端与 Graphlit 服务之间的集成。 除了网络爬取之外,还可以将任何内容(从 Slack 到 Gmail 再到播客订阅源)导入到 Graphlit 项目中,然后从 MCP 客户端检索相关内容。
Kagi MCP Server
一个 MCP 服务器,集成了 Kagi 搜索功能和 Claude AI,使 Claude 能够在回答需要最新信息的问题时执行实时网络搜索。
e2b-mcp-server
使用 MCP 通过 e2b 运行代码。
Neon MCP Server
用于与 Neon 管理 API 和数据库交互的 MCP 服务器
Exa MCP Server
模型上下文协议(MCP)服务器允许像 Claude 这样的 AI 助手使用 Exa AI 搜索 API 进行网络搜索。这种设置允许 AI 模型以安全和受控的方式获取实时的网络信息。