Content
# 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
