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xumingjun5208

Gemini MCP Server

by xumingjun5208
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

Server Configuration

Describes the environment variables required to run the server.

NameRequiredDescriptionDefault
GEMINI_API_KEYNoOptional API key
GEMINI_API_BASE_URLNoAIStudioProxyAPI endpointhttp://127.0.0.1:2048
GEMINI_PROJECT_ROOTNoRoot directory for file resolution$PWD

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

CapabilityDetails
tools
{
  "listChanged": false
}
prompts
{
  "listChanged": false
}
resources
{
  "subscribe": false,
  "listChanged": false
}
experimental
{}

Tools

Functions exposed to the LLM to take actions

NameDescription
gemini_chatA
Send a message to Google Gemini and get a response.

Args:
    params (GeminiChatInput): Chat parameters including:
        - prompt (str): The prompt to send
        - file (Optional[list[str]]): Files to include (text, code, images)
        - session_id (Optional[str]): Session ID for multi-turn chat, use 'last' for recent
        - model (Optional[str]): Override model selection
        - system_prompt (Optional[str]): System context
        - temperature (Optional[float]): Creativity (0.0-2.0)
        - max_tokens (Optional[int]): Max response length
        - response_format: Output format - 'markdown' or 'json'

Returns:
    str: Response with SESSION_ID for continuation.

Examples:
    - Simple: prompt="What is AI?"
    - With file: prompt="Review", file=["main.py"]
    - With image: prompt="Describe", file=["photo.jpg"]
    - Continue: prompt="Tell me more", session_id="last"
gemini_list_modelsB

List available Gemini models.

Prompts

Interactive templates invoked by user choice

NameDescription

No prompts

Resources

Contextual data attached and managed by the client

NameDescription

No resources

Deploy Server
One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

TDQS

A3.6/5.0

Scored across 2 tools

Disambiguation5/5

The two tools have completely distinct purposes with no overlap: gemini_chat handles chat interactions with the Gemini API, while gemini_list_models provides metadata about available models. An agent would never confuse these tools as they serve fundamentally different functions in the workflow.

Naming Consistency5/5

Both tools follow a consistent 'gemini_' prefix + descriptive_snake_case pattern (gemini_chat and gemini_list_models). This creates a predictable naming convention that clearly associates both tools with the Gemini service while maintaining readability and consistency throughout the toolset.

Tool Count2/5

With only 2 tools, this server feels severely under-equipped for a comprehensive Gemini API integration. While chat functionality is essential, the absence of tools for embeddings, file analysis, or other Gemini capabilities creates a thin surface that will limit agent effectiveness. The count is too low for the apparent scope of a full Gemini MCP server.

Completeness2/5

The tool surface is significantly incomplete for a Gemini integration server. While chat functionality is well-implemented, there are major gaps: no tools for embeddings generation, file content analysis beyond chat context, model information beyond listing, or other Gemini API endpoints. This creates dead ends for agents trying to perform common Gemini workflows beyond basic chat interactions.

Maintenance

ActivityInactive
ResponsivenessNo issues

Resources