iflow-mcp_NapthaAI_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 handler…