Gemini MCP Server for Claude Code
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
| LOG_LEVEL | No | Log level (fatal, error, warn, info, debug, trace) | info |
| GEMINI_API_KEY | Yes | Your Gemini API key from Google AI Studio | |
| GEMINI_TIMEOUT_MS | No | Request timeout in milliseconds | 120000 |
| GEMINI_DEFAULT_MODEL | Yes | Gemini model to use for queries |
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 | {} |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| query_geminiA | Query Google's Gemini AI models for text generation, reasoning, and analysis tasks. Use this tool when you need to:
The tool supports conversation history for multi-turn interactions. Streaming is enabled by default for better responsiveness. |
| list_gemini_modelsA | List available Gemini AI models and their capabilities. Use this tool to:
|
| count_gemini_tokensA | Count the number of tokens in a text string for the configured Gemini model. Use this tool to:
|
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 3 tools
Each tool has a clearly distinct purpose with no overlap. count_gemini_tokens handles token counting, list_gemini_models provides model information, and query_gemini performs AI queries. The descriptions reinforce these distinct roles, making misselection unlikely.
All tools follow a consistent verb_noun pattern with snake_case naming. The verbs (count, list, query) are appropriately chosen for their actions, and the noun (gemini) is consistently included, creating a predictable and readable naming convention throughout.
Three tools is reasonable for a Gemini-focused server, covering token counting, model listing, and querying. While slightly minimal, each tool earns its place and provides distinct functionality. A potential fourth tool for model configuration or advanced settings might be missing, but the core operations are well-represented.
The toolset covers the essential Gemini operations: token management, model discovery, and query execution. Minor gaps include lack of direct model configuration tools or advanced query parameters, but agents can work around these. The surface supports basic to intermediate Gemini interactions effectively.