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MCP Gemini

  • Updated June 10, 2026
  • crunchtools/mcp-gemini

MCP Gemini is a MCP server that connects your coding agent to Google Gemini for text, image, video, and research workloads.

About

MCP Gemini is a stdio MCP server that exposes Google Gemini to AI coding agents for text generation, image and video understanding or creation workflows described in the project, and research-oriented prompts. developers who already ship with Claude Code or Cursor but want Gemini’s multimodal stack in the same session install this during Build as agent-tooling, then reuse it in Validate for quick prototypes and in Idea for broader research passes. You remain responsible for Google API credentials, quotas, and acceptable-use policies; the server is the integration shim, not a hosted model. Version 0.3.0 publishes as mcp-gemini-crunchtools on PyPI. Use it when you deliberately want Gemini alongside your primary agent—not when a single built-in model already covers your workflow or you cannot manage API keys securely.

  • MCP bridge to Google Gemini for text, image, video, and research-style tasks
  • stdio transport via PyPI mcp-gemini-crunchtools (v0.3.0)
  • Crunchtools-maintained server with GitHub repo mcp-gemini
  • Fits multi-phase use: research summaries, prototype assets, build-time codegen assist
  • MCP schema 2025-12-11 server manifest for registry discovery

MCP Gemini by the numbers

  • Data as of Jul 7, 2026 (Skillselion catalog sync)
terminal
claude mcp add mcp-gemini-crunchtools -- uvx mcp-gemini-crunchtools

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Packagemcp-gemini-crunchtools
TransportSTDIO
AuthNone
Last updatedJune 10, 2026
Repositorycrunchtools/mcp-gemini

What it does

Wire Google Gemini (text, image, video, research) into your agent via MCP so builds and prototypes can call one multimodal API from the IDE.

Who is it for?

Best when you're standardizing on MCP and want Google Gemini multimodal calls next to your existing agent stack.

Skip if: Skip if you only use Anthropic or OpenAI in-agent with no Google Cloud/API setup, or and need enterprise Gemini governance outside a personal key.

What you get

After registration, the agent can invoke Gemini-backed tools over stdio MCP during prototyping, implementation, and research without custom glue scripts.

  • Agent-invokable Gemini text and multimodal operations via MCP
  • Documented stdio server entry aligned with MCP registry schema
  • Repeatable provider swap-in for build and prototype workflows

By the numbers

  • Server version 0.3.0
  • PyPI identifier mcp-gemini-crunchtools
  • Transport type stdio
README.md

MCP Gemini CrunchTools

A secure MCP (Model Context Protocol) server for Google Gemini AI - text, image, video, research, and more.

Overview

This MCP server is designed to be:

  • Secure by default - Comprehensive threat modeling, input validation, and API key protection
  • No third-party services - Runs locally via stdio, your API key never leaves your machine
  • Cross-platform - Works on Linux, macOS, and Windows
  • Automatically updated - GitHub Actions monitor for CVEs and update dependencies
  • Containerized - Available at quay.io/crunchtools/mcp-gemini built on Hummingbird Python base image

Naming Convention

Component Name
GitHub repo crunchtools/mcp-gemini
Container quay.io/crunchtools/mcp-gemini
Python package (PyPI) mcp-gemini-crunchtools
CLI command mcp-gemini-crunchtools
Module import mcp_gemini_crunchtools

Why Hummingbird?

The container image is built on the Hummingbird Python base image from Project Hummingbird, which provides:

  • Minimal CVE exposure - Hummingbird images are built with a minimal package set, dramatically reducing the attack surface compared to general-purpose images
  • Regular updates - Security patches are applied promptly, keeping CVE counts low
  • Optimized for Python - Pre-configured Python environment with uv package manager for fast, reproducible builds
  • Production-ready - Designed for production workloads with proper signal handling and non-root user defaults

This means your MCP server runs in a hardened environment with fewer vulnerabilities than typical Python container images.

Features

Query Tools (5 tools)

  • gemini_query - Query Gemini with optional Google Search grounding
  • gemini_brainstorm - Generate creative ideas on a topic
  • gemini_analyze_code - Analyze code for security, performance, bugs
  • gemini_analyze_text - Analyze text for sentiment, tone, content
  • gemini_summarize - Summarize content in various formats

Image Generation (4 tools)

  • gemini_generate_image - Generate images from text prompts (native Gemini)
  • gemini_generate_image_with_input - Edit/modify existing images
  • gemini_image_prompt - Craft effective image generation prompts
  • gemini_imagen_generate - Generate images using Google Imagen 4 models

Image Editing (4 tools)

  • gemini_start_image_edit - Start a multi-turn image editing session
  • gemini_continue_image_edit - Continue editing in an active session
  • gemini_end_image_edit - End an image editing session
  • gemini_list_image_sessions - List all active editing sessions

Image Analysis (1 tool)

  • gemini_analyze_image - Analyze and describe local image files

Search Tools (1 tool)

  • gemini_search - Web search using Gemini with Google Search grounding

Document Tools (3 tools)

  • gemini_analyze_document - Analyze PDFs, DOCX, TXT, etc.
  • gemini_summarize_pdf - Summarize PDF documents
  • gemini_extract_tables - Extract tables from documents

URL Tools (3 tools)

  • gemini_analyze_url - Analyze one or more URLs
  • gemini_compare_urls - Compare two URLs
  • gemini_extract_from_url - Extract specific data from a URL

Video Tools (2 tools)

  • gemini_generate_video - Generate videos using Veo
  • gemini_check_video - Check video generation status

YouTube Tools (2 tools)

  • gemini_youtube - Analyze YouTube videos
  • gemini_youtube_summary - Summarize YouTube videos

Voice Tools (3 tools)

  • gemini_speak - Convert text to speech
  • gemini_dialogue - Generate multi-voice dialogue audio
  • gemini_list_voices - List available voices

Research Tools (3 tools)

  • gemini_deep_research - Perform multi-step web research
  • gemini_check_research - Check research operation status
  • gemini_research_followup - Ask follow-up questions

Cache Tools (4 tools)

  • gemini_create_cache - Create content cache for repeated queries
  • gemini_query_cache - Query cached content
  • gemini_list_caches - List all active caches
  • gemini_delete_cache - Delete a cache

Structured Output Tools (2 tools)

  • gemini_structured - Get structured JSON output
  • gemini_extract - Extract structured data from text

Token Tools (1 tool)

  • gemini_count_tokens - Count tokens in content

Code Execution Tools (1 tool)

  • gemini_run_code - Execute Python code via Gemini

Total: 39 tools

Installation

With uvx (Recommended)

uvx mcp-gemini-crunchtools

With pip

pip install mcp-gemini-crunchtools

With Container

# Create a shared output directory (required before first run)
mkdir -p ~/.local/share/mcp-uploads-downloads

podman run -v ~/.local/share/mcp-uploads-downloads:/output:z \
    -e GEMINI_API_KEY=your_key \
    -e GEMINI_OUTPUT_DIR=/output \
    quay.io/crunchtools/mcp-gemini

SELinux note: Use :z (lowercase, shared) instead of :Z (uppercase, private). MCP servers run as long-lived stdio processes. With :Z, files copied into the directory after container start won't have the container's private MCS label and will be invisible inside the container. The :z flag sets a shared container_file_t context that all containers and the host can read/write.

Tip: Use the same shared directory (~/.local/share/mcp-uploads-downloads/) across multiple MCP container servers (e.g., mcp-gemini and mcp-wordpress) so generated images are immediately available for WordPress upload without copying.

Configuration

Creating a Google Gemini API Key

  1. Navigate to Google AI Studio

  2. Create API Key

    • Click "Get API key" or "Create API key"
    • Select a Google Cloud project or create a new one
    • Click "Create API key in new project" (or select existing project)
  3. Copy Your API Key

    • IMPORTANT: Copy the API key immediately - store it securely!
    • The key starts with AI... (e.g., AIzaSy...)

Add to Claude Code

claude mcp add mcp-gemini-crunchtools \
    --env GEMINI_API_KEY=your_api_key_here \
    -- uvx mcp-gemini-crunchtools

Or for the container version:

# Create a shared output directory (required before first run)
mkdir -p ~/.local/share/mcp-uploads-downloads

claude mcp add mcp-gemini-crunchtools \
    --env GEMINI_API_KEY=your_api_key_here \
    --env GEMINI_OUTPUT_DIR=/output \
    -- podman run -i --rm \
        -v ~/.local/share/mcp-uploads-downloads:/output:z \
        -e GEMINI_API_KEY \
        -e GEMINI_OUTPUT_DIR=/output \
        quay.io/crunchtools/mcp-gemini

Optional: Set Output Directory (non-container)

For generated images, audio, and videos when running without a container:

claude mcp add mcp-gemini-crunchtools \
    --env GEMINI_API_KEY=your_api_key_here \
    --env GEMINI_OUTPUT_DIR=$HOME/.local/share/mcp-uploads-downloads \
    -- uvx mcp-gemini-crunchtools

Usage Examples

Query with Google Search

User: What are the latest developments in quantum computing?
Assistant: [calls gemini_query with use_google_search=true]

Generate an Image

User: Generate a photorealistic image of a sunset over mountains
Assistant: [calls gemini_generate_image with prompt and style]

Analyze a PDF Document

User: Analyze this research paper at /path/to/paper.pdf
Assistant: [calls gemini_analyze_document with file_path]

Summarize a YouTube Video

User: Summarize this YouTube video: https://youtube.com/watch?v=...
Assistant: [calls gemini_youtube_summary with url]

Deep Research

User: Research the environmental impact of electric vehicles
Assistant: [calls gemini_deep_research then gemini_check_research]

Code Analysis

User: Analyze this Python code for security issues
Assistant: [calls gemini_analyze_code with focus="security"]

Security

This server was designed with security as a primary concern. See SECURITY.md for:

  • Threat model and attack vectors
  • Defense in depth architecture
  • API key handling best practices
  • Input validation rules
  • Audit logging

Key Security Features

  1. API Key Protection

    • Stored as SecretStr (never accidentally logged)
    • Environment variable only (never in files or args)
    • Sanitized from all error messages
  2. Input Validation

    • Pydantic models for all inputs
    • File path validation
    • URL validation
    • Strict format validation
  3. API Hardening

    • Hardcoded API base URL (prevents SSRF)
    • TLS certificate validation
    • Request timeouts
    • Response size limits
  4. Automated CVE Scanning

    • GitHub Actions scan dependencies weekly
    • Automatic PRs for security updates
    • Dependabot alerts enabled

Development

Setup

git clone https://github.com/crunchtools/mcp-gemini.git
cd mcp-gemini
uv sync

Run Tests

uv run pytest

Lint and Type Check

uv run ruff check src tests
uv run mypy src

Build Container

podman build -t mcp-gemini .

License

AGPL-3.0-or-later

Contributing

Contributions welcome! Please read SECURITY.md before submitting security-related changes.

Links

Recommended MCP Servers

How it compares

MCP model-provider integration, not a prompt library skill or hosted chat UI.

FAQ

Who is io.github.crunchtools/gemini for?

Developers using MCP-enabled agents who want Google Gemini text, image, video, and research capabilities inside Claude Code, Cursor, or similar tools.

When should I use io.github.crunchtools/gemini?

During build and agent-tooling setup, or when validating prototypes and running research that benefit from Gemini multimodal APIs.

How do I add io.github.crunchtools/gemini to my agent?

Install mcp-gemini-crunchtools from PyPI, add the stdio MCP server block in your agent config, and supply valid Google Gemini API credentials per the repo README.

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