automcp

# automcp ## 🚀 Overview automcp allows you to easily convert tools, agents and orchestrators from existing agent frameworks into [MCP](https://modelcontextprotocol.io/introduction) servers, that can then be accessed by standardized interfaces via clients like Cursor and Claude Desktop. We currently support deployment of agents, tools, and orchestrators as MCP servers for the following agent frameworks: 1. CrewAI 2. LangGraph 3. Llama Index 4. OpenAI Agents SDK 5. Pydantic AI 6. mcp-agent ## 🔧 Installation Install from PyPI: ```bash # Basic installation pip install naptha-automcp # UV uv add naptha-automcp ``` Or install from source: ```bash git clone https://github.com/napthaai/automcp.git cd automcp uv venv source .venv/bin/activate pip install -e . ``` ## 🧩 Quick Start Create a new MCP server for your project: Navigate to your project directory with your agent implementation: ```bash cd your-project-directory ``` Generate the MCP server files via CLI with one of the following flags (crewai, langgraph, llamaindex, openai, pydantic, mcp_agent): ```bash automcp init -f crewai ``` Edit the generated `run_mcp.py` file to configure your agent: ```python # Replace these imports with your actual agent classes from your_module import YourCrewClass # Define the input schema class InputSchema(BaseModel): parameter1: str parameter2: str # Set your agent details name = "<YOUR_AGENT_NAME>" description = "<YOUR_AGENT_DESCRIPTION>" # For CrewAI projects mcp_crewai = create_crewai_adapter( orchestrator_instance=YourCrewClass().crew(), name=name, description=description, input_schema=InputSchema, ) ``` Install dependencies and run your MCP server: ```bash automcp serve -t sse ``` ## 📁 Generated Files When you run `automcp init -f <FRAMEWORK>`, the following file is generated: ### run_mcp.py This is the main file that sets up and runs your MCP server. It contains: - Server initialization code - STDIO and SSE transport handlers - A placeholder for your agent implementation - Utilities to suppress warnings that might corrupt the STDIO protocol You'll need to edit this file to: - Import your agent/crew classes - Define your input schema (the parameters your agent accepts) - Configure the adapter with your agent ## 🔍 Examples ### Running the examples The repository includes examples for each supported framework: ```bash # Clone the repository git clone https://github.com/NapthaAI/automcp.git cd automcp # Install automcp in development mode pip install -e . # Navigate to an example directory cd examples/crewai/marketing_agents # Generate the MCP server files (use the appropriate framework) automcp init -f crewai # Edit the generated run_mcp.py file to import and configure the example agent # (See the specific example's README for details) # Add a .env file with necessary environmental variables # Install dependencies and run automcp serve -t sse ``` Each example follows the same workflow as a regular project: 1. Run `automcp init -f <FRAMEWORK>` to generate the server files 2. Edit `run_mcp.py` to import and configure the example agent 3. Add a .env file with necessary environmental variables 4. Install dependencies and serve using `automcp serve -t sse` ### CrewAI example Here's what a typical configured `run_mcp.py` looks like for a CrewAI example: ```python import warnings from typing import Any from automcp.adapters.crewai import create_crewai_adapter from pydantic import BaseModel from mcp.server.fastmcp import FastMCP mcp = FastMCP("MCP Server") warnings.filterwarnings("ignore") from crew import MarketingPostsCrew class InputSchema(BaseModel): project_description: str customer_domain: str name = "marketing_posts_crew" description = "A crew that posts marketing posts to a social media platform" # Create an adapter for crewai mcp_crewai = create_crewai_adapter( orchestrator_instance=MarketingPostsCrew().crew(), name=name, description=description, input_schema=InputSchema, ) mcp.add_tool( mcp_crewai, name=name, description=description ) # Server entrypoints def serve_sse(): mcp.run(transport="sse") def serve_stdio(): # Redirect stderr to suppress warnings that bypass the filters import os import sys class NullWriter: def write(self, *args, **kwargs): pass def flush(self, *args, **kwargs): pass # Save the original stderr original_stderr = sys.stderr # Replace stderr with our null writer to prevent warnings from corrupting STDIO sys.stderr = NullWriter() # Set environment variable to ignore Python warnings os.environ["PYTHONWARNINGS"] = "ignore" try: mcp.run(transport="stdio") finally: # Restore stderr for normal operation sys.stderr = original_stderr if __name__ == "__main__": import sys if len(sys.argv) > 1 and sys.argv[1] == "sse": serve_sse() else: serve_stdio() ``` ## 🔄 Running Your MCP Server After setting up your files, you can run your server using one of these methods: ```bash # Using the automcp CLI automcp serve -t stdio # STDIO transport automcp serve -t sse # SSE transport # Or run the Python file directly python run_mcp.py # STDIO transport python run_mcp.py sse # SSE transport # Or with uv run (if configured in pyproject.toml) uv run serve_stdio uv run serve_sse ``` **Note about transport modes:** - **STDIO**: You don't need to run the server manually - it will be started by the client (Cursor) - **SSE**: This is a two-step process: 1. Start the server separately: `python run_mcp.py sse` or `automcp serve -t sse` 2. Add the mcp.json configuration to connect to the running server If you want to use the `uv run` commands, add the following to your `pyproject.toml`: ```toml [tool.uv.scripts] serve_stdio = "python run_mcp.py" serve_sse = "python run_mcp.py sse" ``` ## ☁️ Deploying with Naptha's MCPaaS Naptha supports deploying your newly-created MCP server to our MCP servers-as-a-service platform! It's easy to get started. ### Setup Naptha's MCPaaS platform requires your repository be set up with `uv`. This means you need a couple configurations in your `pyproject.toml`. First, make sure the `run_mcp.py` file generated by `naptha-automcp` is the root of your repository. Second, make sure your `pyproject.toml` has the following configurations: ```toml [build-system] requires = [ "hatchling",] build-backend = "hatchling.build" [project.scripts] serve_stdio = "run_mcp:serve_stdio" serve_sse = "run_mcp:serve_sse" [tool.hatch.metadata] allow-direct-references = true [tool.hatch.build.targets.wheel] include = [ "run_mcp.py",] exclude = [ "__pycache__", "*.pyc",] sources = [ ".",] packages = ["."] ``` If your agent is in a subdirectory / package of your repository: ``` pyproject.toml run_mcp.py my_agent/ |---| __init__.py | agent.py ``` Make sure that it's imported like this in `run_mcp.py`: ```python from my_agent.agent ``` Not like below, since this will cause the build to fail: ```python from .my_agent.agent ``` Once you have configured everything, commit and push your code (but not your environment variables!) to github. Then, you can test it to make sure you set up everything correctly: ```shell uvx --from https://github.com/your-username/your-repo serve_sse ``` If this results in your MCP server being launched on port 8000 successfully, you're good to go! ### Launching your server 1. go to [labs.naptha.ai](https://labs.naptha.ai) 2. Sign in with your github account 3. Pick the repository you edited from your repository list -- we autodiscover your github repos. 4. add your environment variables e.g. `OPENAI_API_KEY`, etc. 5. Click Launch. 6. Copy the SSE URL, and paste it into your MCP client: ## 🔌 Using with MCP Clients ### Cursor To integrate with Cursor IDE, create a `.cursor` folder in your project root and add an `mcp.json` file with the following configuration: ```json { "mcpServers": { "crew-name-stdio": { "type": "stdio", "command": "/absolute/path/to/your/.venv/bin/uv", "args": [ "--directory", "/absolute/path/to/your/project_dir", "run", "serve_stdio" ], "env": { "OPENAI_API_KEY": "sk-", "SERPER_API_KEY": "" } }, "crew-name-python": { "type": "stdio", "command": "/absolute/path/to/your/.venv/bin/python", "args": [ "/absolute/path/to/your/project_dir/run_mcp.py" ], "env": { "OPENAI_API_KEY": "sk-", "SERPER_API_KEY": "" } }, "crew-name-automcp": { "type": "stdio", "command": "/absolute/path/to/your/.venv/bin/automcp", "args": [ "serve", "-t", "stdio" ], "cwd": "/absolute/path/to/your/project_dir", "env": { "OPENAI_API_KEY": "sk-", "SERPER_API_KEY": "" } }, "crew-name-sse": { "type": "sse", "url": "http://localhost:8000/sse" } } } ``` **Note:** Be sure to replace all placeholder paths with absolute paths to your actual files and directories. ### Direct GitHub Execution Push your project to GitHub and use: ```json { "mcpServers": { "My Agent": { "command": "uvx", "args": [ "--from", "git+https://github.com/your-username/your-repo", "serve_stdio" ], "env": { "OPENAI_API_KEY": "your-key-here" } } } } ``` ## 🛠️ Creating New Adapters Want to add support for a new agent framework? Here's how: 1. Create a new adapter file in automcp/adapters/ (or add to an existing framework file): ```python # automcp/adapters/framework.py import json import contextlib import io from typing import Any, Callable, Type from pydantic import BaseModel def create_framework_adapter( agent_instance: Any, name: str, description: str, input_schema: Type[BaseModel], ) -> Callable: """Doc string for your function""" # Get the field names and types from the input schema schema_fields = input_schema.model_fields # Create the parameter string for the function signature params_str = ", ".join( f"{field_name}: {field_info.annotation.__name__}" for field_name, field_info in schema_fields.items() ) # Create the function body that constructs the input schema # Note: You may need to adjust the method calls (kickoff, model_dump_json) # to match your framework's specific API body_str = f"""def run_agent({params_str}): inputs = input_schema({', '.join(f'{name}={name}' for name in schema_fields)}) with contextlib.redirect_stdout(io.StringIO()): result = agent_instance.framework_specific_run(inputs=inputs.model_dump()) return result.framework_specific_result() """ # Create a namespace for the function namespace = { "input_schema": input_schema, "agent_instance": agent_instance, "json": json, "contextlib": contextlib, "io": io, } # Execute the function definition in the namespace exec(body_str, namespace) # Get the created function run_agent = namespace["run_agent"] # Add proper function metadata run_agent.__name__ = name run_agent.__doc__ = description return run_agent ``` 2. Create an example in examples/your_framework/ ## 📝 Notes - When working with STDIO transport, be careful with print statements in your agent code as they can corrupt the protocol - The MCP Inspector can be used for debugging: `npx @modelcontextprotocol/inspector` - Remember that for STDIO mode, the client (like Cursor) will start the server for you - For SSE mode, you need to manually start the server and then configure the client to connect to it