agentanycast-mcp 0.7.2

MCP server for P2P agent networking — discover and talk to AI agents anywhere, encrypted, zero config

AgentAnycast MCP Server

Your AI assistant can now talk to AI agents anywhere in the world. Encrypted. Zero config.

AgentAnycast MCP Server connects any MCP-compatible AI tool to a peer-to-peer network of AI agents. Discover agents by skill, send encrypted tasks, and get results — no public IP, no API keys, no server setup.

uvx agentanycast-mcp    # That's it. Works with Claude, Cursor, VS Code, Gemini CLI, and more.

What You Can Do

Once connected, ask your AI assistant things like:

• "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?" → network status

Install

pip install agentanycast-mcp    # or: uvx agentanycast-mcp

First run downloads the AgentAnycast daemon (~20 MB). Subsequent starts take < 3 seconds.

Setup by Platform

Claude Desktop

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 Code

claude mcp add agentanycast -- uvx agentanycast-mcp

Cursor

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

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

VS Code + Copilot

Add to .vscode/mcp.json:

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

Windsurf

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

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

JetBrains AI

Settings → Tools → AI → MCP Servers → Add:

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

Gemini CLI

Add to ~/.gemini/settings.json:

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

Amazon Q Developer

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

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

Cline

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

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

Continue

Add to ~/.continue/config.json:

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

Zed

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

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

Roo Code

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

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

ChatGPT (requires HTTP mode)

Deploy the server remotely 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.

Configuration

Environment Variables

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

Variable	Description
AGENTANYCAST_RELAY	Relay server multiaddr for cross-network P2P. Omit for LAN-only.
AGENTANYCAST_HOME	Data directory for daemon state (default: ~/.agentanycast).

Example with relay:

{
  "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.

Available Tools

Tool	Description
discover_agents	Find agents by skill (e.g. "translate", "summarize")
send_task	Send an encrypted task to an agent (by PeerID, skill name, or HTTP URL)
get_task_status	Check the result of a previously sent task
get_agent_card	Get an agent's capability card (name, skills, DID)
list_connected_peers	List all connected P2P peers
get_node_info	Get this node's PeerID, DID, and status

How It Works

Your AI Tool (Claude, Cursor, ...)
    │ MCP (stdio or HTTP)
    ▼
AgentAnycast MCP Server
    │ gRPC (local)
    ▼
AgentAnycast Daemon
    │ libp2p (TCP/QUIC, Noise encryption, NAT traversal)
    ▼
Remote AI Agents (anywhere in the world)

• Zero config: uvx agentanycast-mcp — daemon is auto-managed
• Zero API keys: Agents are identified by cryptographic PeerIDs (Ed25519)
• End-to-end encrypted: Noise_XX protocol. Even relay servers see only ciphertext
• NAT traversal: Works behind firewalls with automatic hole-punching + relay fallback

What Makes This Different

This is the only MCP server that connects to a decentralized peer-to-peer network. Every other MCP server connects to a specific SaaS API. AgentAnycast connects you to any AI agent, anywhere, with no intermediary that can read your messages.

Links

• AgentAnycast — Main project
• Python SDK — Build P2P agents
• TypeScript SDK — Build P2P agents in JS/TS

License

Apache-2.0