MCP Gemini Server

Name: MCP Gemini Server
Author: amitsh06

@amitsh06

Repository

About MCP Gemini Server

No overview available yet

Basic information

Config

Add this server to your MCP-compatible client using the configuration below.

{
  "mcpServers": {
    "mcp-server-amitsh06": {
      "command": "python",
      "args": [
        "-m",
        "venv",
        "venv"
      ]
    }
  }
}

Tools

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Overview

What is MCP Gemini Server?

MCP Gemini Server is an implementation of the Model Context Protocol (MCP) that enables AI assistants like Claude to interact with Google's Gemini API. It allows AI assistants to request text generation, text analysis, and maintain chat conversations through the Gemini API.

How to use MCP Gemini Server?

Install Python 3.7+ and obtain a Google AI API key. Clone the repository, create a virtual environment, install dependencies, and set your API key in a .env file as GEMINI_API_KEY. Start the server with python server.py, then send POST requests to the /mcp endpoint with a JSON payload containing the action and parameters.

Key features of MCP Gemini Server

Implements MCP protocol for secure message exchange.
Generates text via Gemini with configurable temperature and max tokens.
Analyzes text for sentiment, summary, keywords, or general analysis.
Supports multi-turn chat conversations with Gemini.
Uses .env file for secure API key storage.
Provides health check and model listing endpoints.

Use cases of MCP Gemini Server

AI assistants generating creative content like poems or stories.
Analyzing sentiment of user-provided text.
Conducting interactive Q&A sessions with context.
Summarizing documents or extracting keywords.
Enabling Claude to leverage Gemini's text capabilities.

FAQ from MCP Gemini Server

What actions does MCP Gemini Server support?

It supports three actions: generate_text, analyze_text, and chat.

What parameters are required for text generation?

You must provide a prompt. Optional parameters include temperature (0.0 to 1.0) and max_tokens.

What analysis types are available?

The analysis_type parameter can be 'sentiment', 'summary', 'keywords', or 'general'.

How do I start the server?

Run python server.py after installing dependencies and setting your GEMINI_API_KEY in a .env file.

What HTTP endpoints does the server expose?

It exposes GET /health, GET /list-models, and POST /mcp as the main endpoint for MCP requests.

Comments

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