Model Context Protocol (MCP)

by drkhan107

135 downloads

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About

A working pattern for SSE-based MCP clients and servers using Gemini LLM

Details

Author: drkhan107
Downloads: 135
Categories: AI

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

Setting up with Cursor

Open Cursor's settings
Navigate to the MCP section
Click "Add new Global MCP server"
Copy and paste the configuration below based on your operating system

JSON

{
    "mcpServers": {
        "model context protocol (mcp)": {
            "mcp_gemini": {
                "command": "python",
                "args": [
                    "sse_server.py"
                ]
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "model context protocol (mcp)": {
            "mcp_gemini": {
                "command": "python",
                "args": [
                    "sse_server.py"
                ]
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "model context protocol (mcp)": {
            "mcp_gemini": {
                "command": "python",
                "args": [
                    "sse_server.py"
                ]
            }
        }
    }
}

Setting up with Claude Desktop

Open Claude Desktop's settings
Go to the Integrations tab
Click "Add MCP Server"
Use the configuration below based on your operating system

Note: Make sure Claude Desktop is running before attempting to connect.

JSON

{
    "mcpServers": {
        "model context protocol (mcp)": {
            "mcp_gemini": {
                "command": "python",
                "args": [
                    "sse_server.py"
                ]
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "model context protocol (mcp)": {
            "mcp_gemini": {
                "command": "python",
                "args": [
                    "sse_server.py"
                ]
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "model context protocol (mcp)": {
            "mcp_gemini": {
                "command": "python",
                "args": [
                    "sse_server.py"
                ]
            }
        }
    }
}

Setting up with Windsurf

Open Windsurf's preferences
Go to the Integrations section
Click "Add MCP Integration"
Use the configuration below based on your operating system

JSON

{
    "mcpServers": {
        "model context protocol (mcp)": {
            "mcp_gemini": {
                "command": "python",
                "args": [
                    "sse_server.py"
                ]
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "model context protocol (mcp)": {
            "mcp_gemini": {
                "command": "python",
                "args": [
                    "sse_server.py"
                ]
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "model context protocol (mcp)": {
            "mcp_gemini": {
                "command": "python",
                "args": [
                    "sse_server.py"
                ]
            }
        }
    }
}

Setting up with Cline

Open Cline's settings
Navigate to the Integrations section
Click "Add MCP Server"
Copy and paste the configuration below based on your operating system

Note: Make sure to restart Cline after adding the configuration.

JSON

{
    "mcpServers": {
        "model context protocol (mcp)": {
            "mcp_gemini": {
                "command": "python",
                "args": [
                    "sse_server.py"
                ]
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "model context protocol (mcp)": {
            "mcp_gemini": {
                "command": "python",
                "args": [
                    "sse_server.py"
                ]
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "model context protocol (mcp)": {
            "mcp_gemini": {
                "command": "python",
                "args": [
                    "sse_server.py"
                ]
            }
        }
    }
}

Setting up with Goose

Open Goose's preferences
Go to the Integrations tab
Select "MCP Servers"
Click "Add New Server"
Use the configuration below based on your operating system

JSON

{
    "mcpServers": {
        "model context protocol (mcp)": {
            "mcp_gemini": {
                "command": "python",
                "args": [
                    "sse_server.py"
                ]
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "model context protocol (mcp)": {
            "mcp_gemini": {
                "command": "python",
                "args": [
                    "sse_server.py"
                ]
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "model context protocol (mcp)": {
            "mcp_gemini": {
                "command": "python",
                "args": [
                    "sse_server.py"
                ]
            }
        }
    }
}

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 ```bash 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: ```bash pip install -r requirements.txt ``` ### 4. 🖥️ Run the MCP Server Start the MCP server: ```bash python sse_server.py ``` ✅ This will start the MCP server at the configured port (default is http://localhost:8080/sse). ### 5. 🧠 Start the MCP Client (Optional) Once the server is running, start the SSE client with the server URL: ```bash 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) ```bash 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 ```bash 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. ![alt text](image.png) ✅ Done! You now have a working demo of the Model Context Protocol with Gemini.

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Setting up with Highlight

From the repository

Claude Desktop / Cursor

McpServers

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