MCP Gemini CrunchTools
A secure MCP server for Google Gemini AI, providing 39 tools for text, image, video, document, YouTube, research, and more. Runs locally via stdio with API key protection and no third-party services.
README
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
- Google Gemini API Documentation
- Google AI Studio
- FastMCP Documentation
- MCP Specification
- crunchtools.com
<!-- mcp-name: io.github.crunchtools/gemini -->
推荐服务器
Baidu Map
百度地图核心API现已全面兼容MCP协议,是国内首家兼容MCP协议的地图服务商。
Playwright MCP Server
一个模型上下文协议服务器,它使大型语言模型能够通过结构化的可访问性快照与网页进行交互,而无需视觉模型或屏幕截图。
Magic Component Platform (MCP)
一个由人工智能驱动的工具,可以从自然语言描述生成现代化的用户界面组件,并与流行的集成开发环境(IDE)集成,从而简化用户界面开发流程。
Audiense Insights MCP Server
通过模型上下文协议启用与 Audiense Insights 账户的交互,从而促进营销洞察和受众数据的提取和分析,包括人口统计信息、行为和影响者互动。
VeyraX
一个单一的 MCP 工具,连接你所有喜爱的工具:Gmail、日历以及其他 40 多个工具。
graphlit-mcp-server
模型上下文协议 (MCP) 服务器实现了 MCP 客户端与 Graphlit 服务之间的集成。 除了网络爬取之外,还可以将任何内容(从 Slack 到 Gmail 再到播客订阅源)导入到 Graphlit 项目中,然后从 MCP 客户端检索相关内容。
Kagi MCP Server
一个 MCP 服务器,集成了 Kagi 搜索功能和 Claude AI,使 Claude 能够在回答需要最新信息的问题时执行实时网络搜索。
e2b-mcp-server
使用 MCP 通过 e2b 运行代码。
Neon MCP Server
用于与 Neon 管理 API 和数据库交互的 MCP 服务器
Exa MCP Server
模型上下文协议(MCP)服务器允许像 Claude 这样的 AI 助手使用 Exa AI 搜索 API 进行网络搜索。这种设置允许 AI 模型以安全和受控的方式获取实时的网络信息。