> ## Documentation Index
> Fetch the complete documentation index at: https://agentcommunicationprotocol.dev/llms.txt
> Use this file to discover all available pages before exploring further.

# MCP Adapter

> Bridge the gap between ACP and MCP

[**Model Context Protocol (MCP)**](https://modelcontextprotocol.io/introduction) is a popular open standard from Anthropic that provides AI **models** (usually LLMs) with **context**: resources, tools and more.

Agents can be viewed as **MCP resources** and further invoked as **MCP tools**. Such look at ACP agents allows MCP clients to discover and run ACP agents at a cost of reduced interface compared to full ACP.

## Adapter

[ACP-MCP Adapter](https://github.com/i-am-bee/acp-mcp) is a production-ready **MCP server** that can be deployed in front of any **ACP server** to make the aforementioned bridging. It exposes agents as resources and defines tools to run them.

To run the adapter, there are currently two options:

1. Use PyPI package

```sh theme={null}
uvx acp-mcp <acp-server-url> # e.g. http://localhost:8000
```

1. Use Docker image

```sh theme={null}
docker run -i --rm ghcr.io/i-am-bee/acp-mcp <acp-server-url> # e.g. http://host.docker.internal:8000
```

The adapter currently supports [stdio transport](https://modelcontextprotocol.io/docs/concepts/transports#standard-input%2Foutput-stdio). Follow the documentation of your client to integrate one of the commands above (e.g. [Claude tutorial](https://modelcontextprotocol.io/quickstart/user)).