auto-mcp

Name: auto-mcp
Author: NapthaAI

@NapthaAI

Repository Homepage

About auto-mcp

Easily convert tool, agents and orchestrators from existing agent frameworks to MCP servers

#auto-mcp #mcp-servers #automation

Basic information

Config

Add this server to your MCP-compatible client using the configuration below.

{
  "mcpServers": {
    "auto-mcp": {
      "command": "uv",
      "args": [
        "venv"
      ]
    }
  }
}

Tools

No tools detected

We auto-extract tools from the README. The maintainer can list them under a ## Tools heading to populate this section.

Overview

What is auto-mcp?

auto-mcp converts tools, agents, and orchestrators from CrewAI, LangGraph, Llama Index, OpenAI Agents SDK, Pydantic AI, and mcp-agent into MCP servers for standardized access via clients like Cursor and Claude Desktop.

How to use auto-mcp?

Install the package (pip install naptha-automcp), run automcp init -f <framework> in your project directory to generate run_mcp.py, edit it to import your agent and define an input schema with Pydantic, then run automcp serve -t sse or automcp serve -t stdio. The server can also be run directly with Python or uv.

Key features of auto-mcp

Converts existing agent frameworks into MCP servers
Supports CrewAI, LangGraph, Llama Index, OpenAI Agents SDK, Pydantic AI, and mcp-agent
Generates run_mcp.py with STDIO and SSE transport handlers
CLI commands for initialization and server serving
Includes examples for each supported framework
Can deploy to Naptha's MCPaaS platform

Use cases of auto-mcp

Integrate existing CrewAI crews into Cursor IDE
Expose LangGraph agents as MCP tools for Claude Desktop
Deploy Llama Index agents via Naptha's MCPaaS platform
Create standardized MCP endpoints for OpenAI Agents SDK orchestrators

FAQ from auto-mcp

Which agent frameworks does auto-mcp support?

CrewAI, LangGraph, Llama Index, OpenAI Agents SDK, Pydantic AI, and mcp-agent.

What transport modes are available?

STDIO (client starts the server automatically) and SSE (user starts the server separately, then client connects).

How do I avoid STDIO protocol corruption with print statements?

The generated server includes utilities to suppress warnings, and print statements in agent code can corrupt STDIO.

Where can I deploy my auto-mcp server?

You can deploy locally or use Naptha's MCPaaS platform after configuring pyproject.toml correctly.

What are the runtime dependencies?

Python, pip or uv, and the target agent framework (e.g., crewai, langgraph) along with their API keys.

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