"Google Gemini" matching MCP servers:

Enables Codex to delegate routine tasks to Gemini through Google subscription access via Antigravity CLI and have TypeSafe Jev validate results against specified criteria, with batch evaluations and local status checks.

Enables Claude to use Google Gemini as a secondary AI through MCP for large-scale codebase analysis and complex reasoning tasks. Supports both Gemini Flash and Pro models with specialized functions for general queries and comprehensive code analysis.

A Model Context Protocol server that enables AI assistants to interact with Google Gemini CLI, allowing them to leverage Gemini's large token window for analyzing files and codebases using natural language commands.

Token-optimized MCP server that integrates Google Gemini CLI for AI-powered coding assistance with 43.8% token savings through progressive disclosure, supporting multi-turn conversations and multiple Gemini models.

A Model Context Protocol server that enables Claude to interact with Google's Gemini AI models, allowing users to ask Gemini questions directly from Claude.

A Python-based MCP server that enables integration of Gemini AI models with MCP-compatible applications like Cursor/Claude, allowing for interaction with Gemini APIs through the Model Context Protocol.

Integrates Google's Gemini AI models into Claude Code and other MCP clients to provide second opinions, code comparisons, and token counting. It supports streaming responses and multi-turn conversations directly within your existing AI development workflow.

Enables interaction with Google's Gemini AI models including file uploads, conversation management, and batch API processing for large-scale tasks at reduced costs. Supports multiple Gemini models with advanced features like embeddings generation and automated workflow processing.

MCP server that exposes Google Jules AI agent as tools, allowing local AI agents to list authorized repositories and delegate coding tasks to Jules, receiving session URLs for tracking.

An MCP server that lets OpenAI Codex delegate tasks to Google Gemini agents as isolated background workers, with Git worktree isolation, loop detection, and security boundary enforcement.

An MCP server implementation that leverages Google's Gemini API to provide analytical problem-solving capabilities through sequential thinking steps without code generation.

Allows AI assistants to generate and transform high-quality images from text prompts using Google's Gemini model via the MCP protocol.

A TypeScript server that integrates Google's Gemini Pro model with Claude Desktop through the Model Context Protocol, allowing Claude users to access Gemini's text generation capabilities.

A TypeScript MCP server that enables agents to request focused, one-shot consultations with allowlisted Gemini models, with guaranteed model provenance and no silent model changes.

MCP server orchestrating local CLI agents (Claude Code, OpenAI Codex, Google Gemini) for cross-validation, second opinions, and persona-driven prompting.

Enables two-model orchestration where Gemini in Google Antigravity delegates coding tasks to Qwen 3.8 Max in Qwen Studio Desktop via CDP, managing prompt delivery, file generation, continuation, and verification.

A dedicated server that wraps Google's Gemini AI models in a Model Context Protocol (MCP) interface, allowing other LLMs and MCP-compatible systems to access Gemini's capabilities like content generation, function calling, chat, and file handling through standardized tools.

Open-source framework for AI agents (Claude Code, Cursor, Codex, Gemini) to autonomously operate Google Ads, Meta Ads, and Search Console. Grounded in a local STRATEGY.md — not metric-chasing. Defense-in-depth security, local-first. Apache 2.0.

Enables interaction with Google Jules API to automate software development tasks, such as managing sources, sessions, and activities for AI-powered coding assistance.

An MCP server and autonomous agent for Google Ads: build search campaigns, analyse what they're doing, and optionally let it make spend-reducing changes on a schedule without a human in the loop.