Model Context Protocol (MCP)
About
A working pattern for SSE-based MCP clients and servers using Gemini LLM
Details
- Author
- drkhan107
- Downloads
- 135
- Categories
- AI
Jump to
- Integrates with Google’s Gemini.
- Uses SSE (Server-Sent Events) transport.
- Includes a FastAPI server for a GUI backend.
- Provides a Streamlit web interface.
- Simple setup with a .env file for API key.
- Fully functional demo ready to run locally.
Setting up with Highlight
This MCP is not yet compatible with Highlight’s one-click setup. However, you can still use it with Highlight by following these steps:
- Download and install Highlight from highlightai.com/download
- Navigate to the plugins tab and select "Add Custom Plugin"
-
Configure the plugin with the settings below
Plugin Name
Model Context Protocol (MCP)Command (node, npx, python, etc.)Please refer to the README for specific instructions on how to obtain API keys or other required environment variables.
- Enable "Start Automatically" if you want the plugin to start when Highlight launches
From the repository
Clone the repository, set a GOOGLE_API_KEY in a .env file, install dependencies with pip install -r requirements.txt, then start the MCP server with python sse_server.py (defaults to http://localhost:8080/sse). Optionally launch the SSE client with python ssc_client.py http://localhost:8080/sse, start the FastAPI server with python fastapp.py, and finally run the Streamlit app with streamlit run app.py (opens on localhost:8501). Click “Connect to MCP server” in the browser.
Claude Desktop / Cursor
Paste into your MCP client config file to install this server.
{
"mcpServers": {
"model context protocol (mcp)": {
"mcp_gemini": {
"command": "python",
"args": [
"sse_server.py"
]
}
}
}
}McpServers
{
"mcp_gemini": {
"command": "python",
"args": [
"sse_server.py"
]
}
}Model Context Protocol (MCP)
A working demo of MCP integrated with Google's Gemini.
---
🚀 Getting Started
1. Clone the repository
git clone https://github.com/drkhan107/mcp_gemini.git
cd your-repo-name
2. Set up environment variables
Create a .env file in the root directory and add your Google API key:GOOGLE_API_KEY="your_api_key_here"
3. 📦 Install Dependencies
Install all required packages from requirements.txt:pip install -r requirements.txt
4. 🖥️ Run the MCP Server
Start the MCP server:python sse_server.py
5. 🧠 Start the MCP Client (Optional)
Once the server is running, start the SSE client with the server URL:
python ssc_client.py http://localhost:8080/sse
6. 🧠 Start the FastAPI server (To Use GUI)
Run the following command (To change the port etc, edit the fastapp.py file)
python fastapp.py
7. Launch Streamlit app
- Make sure MCP server is running (http://localhost:8080/sse) - Make sure FastAPI is running.Run the following command
streamlit run app.py
This will start the streamlit app on port 8501
8. Browser
Once you open the browser (localhost:8501), click on connect to MCP server.
✅ Done!
You now have a working demo of the Model Context Protocol with Gemini.
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