
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)
claude mcp add mcp-gemini-crunchtools -- uvx mcp-gemini-crunchtoolsAdd your badge
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| Package | mcp-gemini-crunchtools |
|---|---|
| Transport | STDIO |
| Auth | None |
| Last updated | June 10, 2026 |
| Repository | crunchtools/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-geminibuilt 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 groundinggemini_brainstorm- Generate creative ideas on a topicgemini_analyze_code- Analyze code for security, performance, bugsgemini_analyze_text- Analyze text for sentiment, tone, contentgemini_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 imagesgemini_image_prompt- Craft effective image generation promptsgemini_imagen_generate- Generate images using Google Imagen 4 models
Image Editing (4 tools)
gemini_start_image_edit- Start a multi-turn image editing sessiongemini_continue_image_edit- Continue editing in an active sessiongemini_end_image_edit- End an image editing sessiongemini_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 documentsgemini_extract_tables- Extract tables from documents
URL Tools (3 tools)
gemini_analyze_url- Analyze one or more URLsgemini_compare_urls- Compare two URLsgemini_extract_from_url- Extract specific data from a URL
Video Tools (2 tools)
gemini_generate_video- Generate videos using Veogemini_check_video- Check video generation status
YouTube Tools (2 tools)
gemini_youtube- Analyze YouTube videosgemini_youtube_summary- Summarize YouTube videos
Voice Tools (3 tools)
gemini_speak- Convert text to speechgemini_dialogue- Generate multi-voice dialogue audiogemini_list_voices- List available voices
Research Tools (3 tools)
gemini_deep_research- Perform multi-step web researchgemini_check_research- Check research operation statusgemini_research_followup- Ask follow-up questions
Cache Tools (4 tools)
gemini_create_cache- Create content cache for repeated queriesgemini_query_cache- Query cached contentgemini_list_caches- List all active cachesgemini_delete_cache- Delete a cache
Structured Output Tools (2 tools)
gemini_structured- Get structured JSON outputgemini_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:zflag sets a sharedcontainer_file_tcontext 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
Navigate to Google AI Studio
- Go to https://aistudio.google.com/apikey
- Sign in with your Google account
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)
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
API Key Protection
- Stored as SecretStr (never accidentally logged)
- Environment variable only (never in files or args)
- Sanitized from all error messages
Input Validation
- Pydantic models for all inputs
- File path validation
- URL validation
- Strict format validation
API Hardening
- Hardcoded API base URL (prevents SSRF)
- TLS certificate validation
- Request timeouts
- Response size limits
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.