i-am-bee/acp-mcp

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🐍 💬 - An MCP server acting as an adapter into the ACP ecosystem. Seamlessly exposes ACP agents to MCP clients, bridging the communication gap between the two protocols.

Quick Install

One-Click IDE Configuration
claude_desktop_config.json
{
  "mcpServers": {
    "i-am-bee-acp-mcp": {
      "command": "npx",
      "args": [
        "-y",
        "i-am-bee-acp-mcp"
      ]
    }
  }
}
Or

Using an AI coding agent (Claude Code, Cursor, etc.)? Copy a ready-made prompt that tells it to fetch the setup instructions and install this server for you.

Documentation Overview

ACP to MCP Adapter

Connect ACP Agents to MCP Applications Seamlessly

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The ACP to MCP Adapter is a lightweight standalone server that acts as a bridge between two AI ecosystems: Agent Communication Protocol (ACP) for agent-to-agent communication and Model Context Protocol (MCP) for connecting AI models to external tools. It allows MCP applications (like Claude Desktop) to discover and interact with ACP agents as resources.

Capabilities & Tradeoffs

This adapter enables interoperability between ACP and MCP with specific benefits and tradeoffs:

Benefits

  • Makes ACP agents discoverable as MCP resources
  • Exposes ACP agent runs as MCP tools
  • Bridges two ecosystems with minimal configuration

Current Limitations

  • ACP agents become MCP tools instead of conversational peers
  • No streaming of incremental updates
  • No shared memory across servers
  • Basic content translation between formats without support for complex data structures

This adapter is best for situations where you need ACP agents in MCP environments and accept these compromises.

Requirements

  • Python 3.11 or higher
  • Installed Python packages: acp-sdk, mcp
  • An ACP server running (Tip: Follow the ACP quickstart to start one easily)
  • An MCP client application (We use Claude Desktop in the quickstart)

Quickstart

1. Run the Adapter

Start the adapter and connect it to your ACP server:

uvx acp-mcp http://localhost:8000

[!NOTE] Replace http://localhost:8000 with your ACP server URL if different.

Prefer Docker?
docker run -i --rm ghcr.io/i-am-bee/acp-mcp http://host.docker.internal:8000

Tip: host.docker.internal allows Docker containers to reach services running on the host (adjust if needed for your setup).

2. Connect via Claude Desktop

To connect via Claude Desktop, follow these steps:

  1. Open the Claude menu on your computer and navigate to Settings (note: this is separate from the in-app Claude account settings).
  2. Navigate to Developer > Edit Config
  3. The config file will be created here:
  • macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
  • Windows: %APPDATA%\Claude\claude_desktop_config.json
  1. Edit the file with the following:
{
  "mcpServers": {
    "acp-local": {
      "command": "uvx",
      "args": ["acp-mcp", "http://localhost:8000"]
    }
  }
}
Prefer Docker?
{
  "mcpServers": {
    "acp-docker": {
      "command": "docker",
      "args": [
        "run",
        "-i",
        "--rm",
        "ghcr.io/i-am-bee/acp-mcp",
        "http://host.docker.internal:8000"
      ]
    }
  }
}

3. Restart Claude Desktop and Invoke Your ACP Agent

After restarting, invoke your ACP agent with:

use "echo" agent with the "Good morning!" input

Accept the integration and observe the agent running.

Screenshot of Claude Desktop invoking the echo agent

MCP Resources

[!TIP] ACP agents are also registered as MCP resources in Claude Desktop.
To attach them manually, click the Resources icon (two plugs connecting) in the sidebar, labeled "Attach from MCP", then select an agent like acp://agents/echo.

How It Works

  1. The adapter connects to your ACP server.
  2. It automatically discovers all registered ACP agents.
  3. Each ACP agent is registered in MCP as a resource using the URI: acp://agents/{agent_name}
  4. The adapter provides a new MCP tool called run_agent, letting MCP apps easily invoke ACP agents.

Supported Transports

  • Currently supports Stdio transport

Developed by contributors to the BeeAI project, this initiative is part of the Linux Foundation AI & Data program. Its development follows open, collaborative, and community-driven practices.

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