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

Related MCP server: Gemini Research MCP Server

Naming Convention

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

   • Go to https://aistudio.google.com/apikey

   • Sign in with your Google account

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

• Google Gemini API Documentation

• Google AI Studio

• FastMCP Documentation

• MCP Specification

• crunchtools.com