MCP Server Gemini
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
| NODE_ENV | No | Environment mode (production or development) | production |
| LOG_LEVEL | No | Logging level (error, warn, info, debug) | info |
| ENABLE_METRICS | No | Enable performance metrics | false |
| GEMINI_API_KEY | Yes | Google AI Studio API Key for accessing Gemini models | |
| REQUEST_TIMEOUT | No | Request timeout in milliseconds (30000 = 30 seconds) | 30000 |
| RATE_LIMIT_WINDOW | No | Time window in ms (60000 = 1 minute) | 60000 |
| RATE_LIMIT_ENABLED | No | Enable/disable rate limiting | true |
| RATE_LIMIT_REQUESTS | No | Max requests per window | 100 |
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.
Most tools follow a consistent verb_noun pattern (analyze_image, count_tokens, embed_text, generate_text, list_models), but get_help deviates slightly with a 'get_' prefix. The naming is still highly readable and predictable overall.
With 6 tools, this server is well-scoped for Gemini AI interactions. Each tool earns its place by covering distinct aspects: vision, token management, embeddings, text generation, help, and model discovery, without being overly sparse or bloated.
The toolset covers core Gemini AI workflows comprehensively, including text and image processing, embeddings, and model management. A minor gap exists in lacking explicit tools for chat/multi-turn conversations or file uploads, but agents can work around this using generate_text and analyze_image.