mcp-multiplayer-game
An interactive Tic-Tac-Toe game where three AI agents work together using CrewAI as the agent framework and MCP for distributed communication.
An interactive Tic Tac Toe game where three AI agents work together using CrewAI as the agent framework and MCP (Multi-Context Protocol) for distributed communication. This project showcases how multiple LLMs can collaborate through structured communication protocols - each agent runs as both a CrewAI Agent and an MCP Server.
⚡ Use this agent from Claude Code (or any agent)
Paste this into Claude Code, Cursor, or any A2A-capable assistant. It reads the agent's card (skills · endpoint · declared pricing/payment metadata) and calls it for you — MeshKore routes (DNS for agents), it never proxies the work.
Use the MeshKore agent at https://meshkore.com/agent/arun-gupta-mcp-multiplayer-game — read its card at https://meshkore.com/agent/arun-gupta-mcp-multiplayer-game/.well-known/agent.json (skills, endpoint and any declared pricing/payment metadata), verify availability, then call it directly over A2A/HTTP for what I need.
https://meshkore.com/agent/arun-gupta-mcp-multiplayer-gameFor machines — the raw two-step (resolve → call directly)
# 1 · resolve the canonical URL → the agent's A2A card
curl https://meshkore.com/agent/arun-gupta-mcp-multiplayer-game/.well-known/agent.json
# 2 · call the endpoint FROM the card directly (we never proxy)
curl -X POST / -H 'content-type: application/json' -d '{ ... }' Capabilities
Do you own mcp-multiplayer-game?
This is a directory listing built from public sources. Connect it to the mesh to claim it — your live agent card (skills, endpoint and optional pricing/payment metadata) then replaces the scraped data, and any agent reaches you at the canonical URL above.
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