Gemini MCP Server
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
| GEMINI_API_KEY | Yes | Your Gemini API key used to authenticate with Google's Gemini AI models. |
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
Server capabilities have not been inspected yet.
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| generate_textC | Generate text using Google Gemini with advanced features |
| analyze_imageC | Analyze images using Gemini vision capabilities |
| count_tokensC | Count tokens for a given text with a specific model |
| list_modelsB | List all available Gemini models and their capabilities |
| embed_textC | Generate embeddings for text using Gemini embedding models |
| get_helpC | Get help and usage information for the Gemini MCP server |
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
| code_review | Comprehensive code review with Gemini 2.5 Pro |
| explain_with_thinking | Deep explanation using Gemini 2.5 thinking capabilities |
| creative_writing | Creative writing with style control |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
| Available Gemini Models | List of all available Gemini models and their capabilities |
| API Capabilities | Detailed information about Gemini API capabilities |
| Usage Guide | Complete guide on using all tools and features |
| Parameters Reference | Detailed documentation of all parameters |
| Examples | Example usage patterns for common tasks |
TDQS
Scored across 6 tools
Each tool has a clearly distinct purpose with no overlap: image analysis, token counting, text embedding, text generation, help retrieval, and model listing. The descriptions reinforce these distinct functions, making tool selection unambiguous for an agent.
All tool names follow a consistent verb_noun pattern (e.g., analyze_image, count_tokens, embed_text), using snake_case throughout. This predictable naming scheme enhances readability and usability for agents.
With 6 tools, the server is well-scoped for its purpose of providing Gemini AI capabilities. Each tool serves a specific, essential function (e.g., core generation, analysis, and utility tasks), with no redundant or trivial additions.
The tool set covers key Gemini functionalities like text generation, image analysis, embeddings, and model listing, with a helpful utility tool. A minor gap is the lack of tools for managing conversations or multi-turn interactions, but core workflows are well-supported.