AgentAnycast MCP Server

Turn any AI tool into a peer-to-peer agent hub. Discover, communicate with, and orchestrate AI agents across any network -- encrypted, decentralized, zero config.

uvx agentanycast-mcp

Works with Claude Desktop, Claude Code, Cursor, VS Code, Windsurf, JetBrains, Gemini CLI, Amazon Q, Cline, Continue, Zed, Roo Code, and ChatGPT.

Setup

Pick your platform and add the config below. That's the entire setup -- the daemon downloads and starts automatically on first run.

Add to ~/Library/Application Support/Claude/claude_desktop_config.json (macOS) or %APPDATA%\Claude\claude_desktop_config.json (Windows):

{
  "mcpServers": {
    "agentanycast": {
      "command": "uvx",
      "args": ["agentanycast-mcp"]
    }
  }
}

claude mcp add agentanycast -- uvx agentanycast-mcp

Add to .cursor/mcp.json in your project root:

{
  "mcpServers": {
    "agentanycast": {
      "command": "uvx",
      "args": ["agentanycast-mcp"]
    }
  }
}

Add to .vscode/mcp.json:

{
  "servers": {
    "agentanycast": {
      "command": "uvx",
      "args": ["agentanycast-mcp"]
    }
  }
}

Add to ~/.codeium/windsurf/mcp_config.json:

{
  "mcpServers": {
    "agentanycast": {
      "command": "uvx",
      "args": ["agentanycast-mcp"]
    }
  }
}

Settings -> Tools -> AI -> MCP Servers -> Add:

{
  "servers": {
    "agentanycast": {
      "command": "uvx",
      "args": ["agentanycast-mcp"]
    }
  }
}

Add to ~/.gemini/settings.json:

{
  "mcpServers": {
    "agentanycast": {
      "command": "uvx",
      "args": ["agentanycast-mcp"]
    }
  }
}

Add to ~/.aws/amazonq/mcp.json:

{
  "mcpServers": {
    "agentanycast": {
      "command": "uvx",
      "args": ["agentanycast-mcp"]
    }
  }
}

Add to Cline MCP settings (VS Code: Ctrl+Shift+P -> "Cline: MCP Servers"):

{
  "mcpServers": {
    "agentanycast": {
      "command": "uvx",
      "args": ["agentanycast-mcp"]
    }
  }
}

Add to ~/.continue/config.json:

{
  "experimental": {
    "modelContextProtocolServers": [
      {
        "transport": {
          "type": "stdio",
          "command": "uvx",
          "args": ["agentanycast-mcp"]
        }
      }
    ]
  }
}

Add to Zed settings (~/.config/zed/settings.json):

{
  "context_servers": {
    "agentanycast": {
      "command": {
        "path": "uvx",
        "args": ["agentanycast-mcp"]
      }
    }
  }
}

Add to Roo Code MCP settings (VS Code: Ctrl+Shift+P -> "Roo Code: MCP Servers"):

{
  "mcpServers": {
    "agentanycast": {
      "command": "uvx",
      "args": ["agentanycast-mcp"]
    }
  }
}

Deploy the server with HTTP transport:

agentanycast-mcp --transport http --port 8080
# or: docker run -p 8080:8080 agentanycast/mcp-server

Then add http://your-server:8080/mcp in ChatGPT developer settings.

What You Can Do

Once connected, ask your AI assistant:

• "Find agents that can translate Japanese" -- discovers agents on the P2P network

• "Send 'summarize this article' to the translate agent" -- encrypted task delivery

• "What agents are connected right now?" -- lists connected peers

• "What's my Peer ID?" -- shows your node's identity and DID

Available Tools

Configuration

Environment Variables

Set these in the "env" section of your MCP config:

Example with relay for cross-network communication:

{
  "mcpServers": {
    "agentanycast": {
      "command": "uvx",
      "args": ["agentanycast-mcp"],
      "env": {
        "AGENTANYCAST_RELAY": "/ip4/relay.agentanycast.io/tcp/4001/p2p/12D3KooW..."
      }
    }
  }
}

CLI Arguments

agentanycast-mcp [--transport stdio|http] [--port 8080] [--relay MULTIADDR] [--home DIR]

CLI arguments take priority over environment variables.

How It Works

Your AI Tool (Claude, Cursor, VS Code, ...)
    |
    | MCP protocol (stdio or HTTP)
    v
AgentAnycast MCP Server
    |
    | gRPC (Unix domain socket)
    v
AgentAnycast Daemon (Go)
    |
    | libp2p (TCP/QUIC + Noise_XX encryption + NAT traversal)
    v
Remote AI Agents (anywhere in the world)

• Zero config -- uvx agentanycast-mcp handles everything. The daemon is auto-downloaded and managed.

• Zero API keys -- agents are identified by cryptographic Peer IDs (Ed25519), not accounts or tokens.

• End-to-end encrypted -- Noise_XX protocol. Even relay servers see only ciphertext.

• NAT traversal -- works behind firewalls with automatic hole-punching and relay fallback.

What Makes This Different

This is the only MCP server that connects to a decentralized peer-to-peer network of AI agents. Other MCP servers connect to specific SaaS APIs. AgentAnycast connects you to any AI agent, anywhere, with no intermediary that can read your messages.

Troubleshooting

Daemon fails to start

• Check that port 4001 (TCP) is not in use: lsof -i :4001

• Try a clean state: rm -rf ~/.agentanycast && uvx agentanycast-mcp

No agents found on discover

• Agents must be on the same LAN (mDNS) or connected to the same relay

• Set AGENTANYCAST_RELAY to connect across networks

Connection timeout

• Behind a strict firewall? Set a relay address. The relay provides fallback connectivity.

• Check daemon logs: cat ~/.agentanycast/daemon.log

"uvx" not found

• Install uv: curl -LsSf https://astral.sh/uv/install.sh | sh

• Or install directly: pip install agentanycast-mcp

Tool calls failing

• Restart your AI tool after adding the MCP config

• Verify config JSON syntax (no trailing commas)

Links

• AgentAnycast -- Main project, documentation, examples

• Python SDK -- Build P2P agents in Python

• TypeScript SDK -- Build P2P agents in TypeScript

License

Apache License, Version 2.0