mcp-sse-demo

# MCP SSE Demo This repository demonstrates using the [Model Context Protocol (MCP)](https://modelcontextprotocol.io/) with Server-Sent Events (SSE) to interact with a Zapier MCP server, enabling access to Gmail functionalities through an AI agent. It illustrates integrating a remotely hosted MCP server with your LLM of choice (i.e. not via the Claude desktop app or Cursor) Update: SSE is being deprecated in favor of Stremable HTTP. I've added an example using this via FastMCP. [Notebook here](fastmcp-example.ipynb) ## Overview The project uses an OpenAI model (`gpt-4o-mini`) to understand user requests, determine if a Gmail action (tool) is needed, and execute it via the Zapier MCP server. ## Key Files * `mcp_clients/zapier_mcp_client.py`: Contains the `GmailMCPClient` class responsible for connecting to the Zapier MCP server over SSE, listing available tools, and managing the connection. * `chat.py`: The chat application logic. It initializes the `GmailMCPClient`, connects to the Zapier server, retrieves tools, execute the tool call and pass the tool output to the LLM for synthesis. (i.e. the familiar function calling pattern) ## Prerequisites - Python 3.8+ - OpenAI API key - Zapier account with MCP access ## Setup 1. **Clone the repository:** ```bash git clone <your-repo-url> cd <your-repo-directory> ``` 2. **Create a virtual environment and install dependencies:** ```bash python -m venv .venv source .venv/bin/activate pip install openai python-dotenv mcp nest_asyncio fastmcp ``` 3. **Create a `.env` file:** ``` touch .env ``` 4. **Add your credentials to the `.env` file:** ```dotenv OPENAI_API_KEY="sk-proj-..." ZAPIER_URL="https://actions.zapier.com/mcp/YOUR_SECRET_KEY/sse" ZAPIER_URL_FASTMCP="https://mcp.zapier.com/api/mcp/s/your-secret-key/mcp" 5. **Enable the Zapier actions you want to call as tools** via https://actions.zapier.com/settings/mcp/ This demo uses `Gmail: Find Email` ## Usage The `chat.py` script provides an async `chat(user_input)` function. You can import and use this function in your own script or an interactive environment i.e. Jupyter notebook to interact with the chatbot. Example in a Python script): ```python import asyncio from chat import chat async def main(): user_query = "Check my latest email." response = await chat(user_query) print(response) if __name__ == "__main__": asyncio.run(main()) ``` Example in Jupyter notebook  Example using the FastMCP framework (tool discovery and invocation) * [full notebook can be found here](fastmcp-example.ipynb)