Gemini MCP Server
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
| GEMINI_API_KEY | Yes | Your Google Gemini API key |
Instructions
Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.
This server publishes no instructions, or was last inspected before Glama recorded them.
Capabilities
Features and capabilities supported by this server
Protocol revision2025-11-25
| Capability | Details |
|---|---|
| tools | {
"listChanged": true
} |
| logging | {} |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| generate_textB | Generate text using Google Gemini models with configurable model, temperature, and system instructions. |
| chatA | Multi-turn conversation with session management. Omit sessionId to start a new session; include it to continue an existing one. |
| generate_with_searchA | Generate text with Google Search grounding for up-to-date, cited responses. |
| code_executionA | Execute Python code in a sandboxed environment. Gemini generates and runs code, returning both the code and results. |
| generate_imageB | Generate an image from a text prompt using Gemini image models (Nano Banana Pro by default). |
| edit_imageA | Edit an image using a text prompt. Send a base64-encoded image and describe the desired changes. |
| edit_image_multiB | Edit or compose images using multiple reference images (up to 14). Uses gemini-3-pro-image-preview (Nano Banana Pro). |
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
No prompts | |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
No resources | |
TDQS
Scored across 7 tools
Each tool has a clearly distinct purpose with no overlap: chat handles conversations, code_execution runs Python, edit_image modifies single images, edit_image_multi handles multiple images, generate_image creates images, generate_text produces text, and generate_with_search adds search grounding. The descriptions clearly differentiate their functions, making misselection unlikely.
All tools follow a consistent verb_noun pattern (e.g., chat, code_execution, edit_image, generate_image, generate_text, generate_with_search). The naming is uniform and predictable across all seven tools, with no deviations in style or convention.
With 7 tools, the count is well-scoped for a Gemini MCP server, covering core AI functionalities like text generation, image handling, code execution, and chat. Each tool earns its place without feeling excessive or insufficient for the server's purpose.
The tool surface is nearly complete for a Gemini AI server, covering text generation (with and without search), image generation and editing, code execution, and chat. A minor gap exists in lacking explicit tools for model management or configuration, but core workflows are well-covered and agents can work around this.