Gemini MCP Server
G

Gemini MCP Server

A Gemini model - integrated MCP server based on FastMCP, implemented in Python and deployed via Docker
2 points
6.9K

What is the Gemini MCP Server?

The Gemini MCP Server is a Model Context Protocol (MCP) server - side program based on FastMCP. It allows users to connect the Gemini model to applications that support the MCP protocol, such as Cursor or Claude. Through this server, tasks like text generation and dialogue interaction can be easily carried out.

How to Use the Gemini MCP Server?

This server can be run in a Docker container. It requires configuration information such as the Gemini API key, model name, and base URL. Users only need to configure and start the service as instructed to use the Gemini model on platforms that support MCP.

Applicable Scenarios

Suitable for developers who want to integrate the Gemini model into other applications and teams that need to quickly deploy MCP services. For example, the Gemini model can be used in code editors (such as Cursor) for intelligent suggestions or code generation.

Main Features

Support for the Gemini Model
Capable of connecting to Google's Gemini model to perform natural language processing and text generation tasks.
Containerized Deployment
Deployed via Docker, which simplifies the installation and management process and is suitable for various development environments.
Compatibility with the MCP Protocol
Fully adheres to the Model Context Protocol (MCP) standard, ensuring seamless integration with other MCP - supporting applications.
Advantages
Easy to integrate into existing systems and supports multiple development tools.
Deployed via Docker, facilitating maintenance and expansion.
Flexible selection of different Gemini models for invocation.
Limitations
Requires a Gemini API key, which may incur costs.
Depends on network connectivity and cannot be used in offline environments.
The deployment process may be complex for users unfamiliar with Docker.

How to Use

Build the Docker Image
Run the command 'docker build -t gemini - mcp - server .' in the project root directory to build the Docker image.
Run the Docker Container
Use the 'docker run' command to start the container and pass in necessary environment variables, such as the API key and model name.
Configure the MCP Server
Add the Gemini MCP Server configuration in the target application (such as Cursor) and enter the correct parameters.

Usage Examples

Use the Gemini Model in Cursor
Implement AI - assisted code writing and intelligent suggestion functions in Cursor through the Gemini MCP Server.
Invoke Gemini in a Chatbot
Enable the chatbot to answer user questions and generate natural - language responses using the Gemini MCP Server.

Frequently Asked Questions

What information do I need to run the Gemini MCP Server?
Why can't my Gemini MCP Server start?
Does it support multiple languages?

Related Resources

Gemini Official Documentation
Understand detailed information about the Gemini model and how to use its API.
FastMCP GitHub Repository
Source code and development documentation for the FastMCP project.
Docker Official Documentation
Learn how to use Docker for containerized deployment.

Installation

Copy the following command to your Client for configuration
{
  "mcpServers": {
    "gemini": {
      "command": "docker",
      "args": [
        "run",
        "--rm",
        "-i",
        "--network",
        "host",
        "-e",
        "GEMINI_API_KEY",
        "-e",
        "GEMINI_MODEL",
        "-e",
        "GEMINI_BASE_URL",
        "-e",
        "HTTP_PROXY",
        "-e",
        "HTTPS_PROXY",
        "gemini-mcp-server:latest"
      ],
      "env": {
        "GEMINI_API_KEY":"your_api_key_here",
        "GEMINI_MODEL":"gemini-2.5-flash",
        "GEMINI_BASE_URL":"https://generativelanguage.googleapis.com/v1beta/openai/",
        "HTTP_PROXY":"http://127.0.0.1:17890",
        "HTTPS_PROXY":"http://127.0.0.1:17890"

      }
    }
  }
}
Note: Your key is sensitive information, do not share it with anyone.

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