Gemini MCP Server
Provides tools for interacting with the Google Gemini API, enabling text generation, multi-turn chat, image analysis, text embeddings, token counting, and model discovery.
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@Gemini MCP ServerGenerate a summary of the latest AI news"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
Gemini MCP Server
A small, clean Model Context Protocol server that connects any MCP client — Claude Desktop, Cursor, Cline, Cowork — to the Google Gemini API.
One Python file, six tools, no framework lock-in. Bring your own Gemini key and you can generate text, hold multi-turn chats, understand images, embed text, and count tokens — straight from your assistant.
✨ Tools
Tool | What it does |
| Single-turn text generation (system instruction, temperature, JSON mode) |
| Multi-turn conversation with full history |
| Analyze / OCR / describe an image (base64 + prompt) |
| Text embeddings for search, clustering, RAG |
| Count tokens for cost & context-window planning |
| Discover available models and their limits |
Related MCP server: Gemini MCP Server
🚀 Quick start
Option A — one-click (Windows, Claude Desktop)
# in the repo folder, right-click install.ps1 -> "Run with PowerShell"
./install.ps1It asks for your API key, installs dependencies, and wires up Claude Desktop. Restart Claude and you're done.
Option B — manual (any OS / any MCP client)
git clone https://github.com/deepzhun/gemini-mcp-server.git
cd gemini-mcp-server
pip install -r requirements.txt
export GEMINI_API_KEY="your-api-key" # Windows: setx GEMINI_API_KEY "your-api-key"
python gemini_mcp.pyGet a free API key at https://ai.google.dev/gemini-api/docs/api-key.
🔌 Add to your MCP client
Copy config.example.json and drop the server block into your client config
(Claude Desktop: claude_desktop_config.json), replacing the placeholder key:
{
"mcpServers": {
"gemini": {
"command": "python",
"args": ["/absolute/path/to/gemini_mcp.py"],
"env": { "GEMINI_API_KEY": "YOUR_GEMINI_API_KEY_HERE" }
}
}
}💬 Usage examples
Once connected, just ask your assistant in natural language:
"Use gemini to summarize this article in three bullet points."
"Ask gemini-2.5-pro to refactor this function and explain the change."
"Use gemini_vision to read the text in this screenshot."
"Embed these 20 product descriptions with gemini for similarity search."
🔐 Security
The key is read from the
GEMINI_API_KEY(orGOOGLE_API_KEY) environment variable — never hard-code it.config.jsonand.envare git-ignored so a real key can't be committed by accident.Rotate your key at Google AI Studio if it is ever exposed.
🛠️ Development
pip install -r requirements.txt
python -c "import gemini_mcp; print(gemini_mcp.mcp.name)" # smoke testThe whole server is a single file (gemini_mcp.py): typed Pydantic inputs,
consistent error handling, and MCP tool annotations. Easy to read, easy to fork.
📄 License
MIT © deepzhun (深准). Contributions welcome — open an issue or PR.
This server cannot be deployed
Maintenance
Related MCP Connectors
Connect MCP clients to 2,000+ AI models without managing provider API keys.
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One MCP endpoint for Claude, GPT & Gemini: 100+ tools + no-code connectors + agent workers.
One connector for 15,000+ MCP servers plus your team's private MCPs, from any AI client.
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