The Gemini MCP Server is a middleware designed for the Claude desktop application, enabling image generation through Google's Gemini AI model. This project provides a convenient installation and configuration wizard, supports Docker containerized deployment, and allows users to customize generation parameters.

A Blender MCP client application based on Firebase Genkit and Gemini, supporting the creation and manipulation of 3D scenes by selecting shapes, colors, textures, and scene descriptions, and enabling real-time interaction with Blender.

This project implements an MCP server for interacting with the Wolfram Alpha API, enabling chat applications to perform calculation queries and obtain structured knowledge. It includes an MCP client example based on Gemini, demonstrating how to connect a large - language model to the MCP server for real - time interaction with the Wolfram Alpha knowledge engine.

A basic MCP application that combines a FastAPI backend and a Gradio frontend, supporting interaction with AI models through a simple interface and integrating the Google Gemini API to implement paper search and analysis functions.

OmniMind is an open - source Python library designed to simplify Model Context Protocol (MCP) integration and support the development of AI agents, workflows, and automation. It offers plug - and - play functionality, has built - in multiple tools, and provides intelligent responses based on Google Gemini, making it suitable for developers and beginners to quickly build AI applications.

The combination of Gemini CLI and the MCP server built with Google Apps Script extends the automation capabilities of Google Workspace through a low - code platform, providing support for 41 tools covering services such as Gmail, Drive, Calendar, etc., and enabling multi - step execution of complex tasks.

This project implements an MCP server that integrates Venice AI's image - generation capabilities, providing an interactive image - generation service with an approval/regeneration workflow and supporting the generation of multi - view 3D views through the Gemini API.

A Python application integrating mathematical calculations and Gmail functions, which uses Gemini AI to handle mathematical problems and sends results via email

This project uses advanced AI prompting techniques to enable an AI agent (based on Google Gemini model) to interact with the legacy Windows application MSPaint through the Model Context Protocol (MCP) to solve complex math problems and draw solutions on the canvas.

A modern to-do list application based on FastAPI and the MCP protocol, supporting complete CRUD operations, task statistics, and integrating with the Gemini CLI through the FastMCP server to achieve natural language task management.

A Todo application based on natural language processing that uses the Google Gemini API to parse commands, MongoDB to store data, and achieves real-time updates through the MCP protocol.

A cognitive dynamic application security testing automation system based on OWASP ZAP and Google Gemini AI. It integrates AI analysis through the MCP protocol, providing automated scanning, intelligent risk assessment, and multi - format report output.

A Python application that combines mathematical calculation and drawing functions, processes mathematical problems through Gemini AI, and visualizes the results

An example project that includes various AI agents and RAG technologies, covering Gemini applications, MCP services, Flutter applications, and Vertex AI integration, providing AI development examples from basic to advanced.

The Gemini MCP Server is a single executable file developed in Go, integrating the Model Control Protocol server of Google Gemini API. It provides dynamic model access, advanced context caching, intelligent file handling, and enhanced search functions, supporting various application scenarios such as code analysis, creative writing, and factual research.