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Gemini MCP Server

Gemini MCP Server

by dakrin
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JavaScript
Remote

Gemini MCP Server

Model Context Protocol (MCP) server implementation that enables Claude Desktop to interact with Google's Gemini 2.5 Pro Experimental AI model.

Features

  • Full MCP protocol support

  • Google Gemini 2.5 Pro Experimental model access

  • Secure API key handling

  • Google Search integration (optional)

  • Token usage reporting

  • TypeScript implementation

Related MCP server: MCP Gemini Server

Available Tools

The MCP server provides the following tools:

  1. generateWithGemini - Generate content with Google Gemini 2.5 Pro Experimental

    • Parameters:

      • prompt (string, required): The prompt to send to Gemini

      • temperature (number, optional): Temperature setting (0.0 to 1.0)

      • maxTokens (number, optional): Maximum output tokens

      • safeMode (boolean, optional): Enable safe mode for sensitive topics

      • useSearch (boolean, optional): Enable Google Search grounding tool

  2. getModelInfo - Get information about the Gemini model being used

Common Issues

  1. Connection Issues

    • Check that you have a valid API key

Security

  • API keys are handled via environment variables only

  • No sensitive data is logged or stored

Available Tools

2 tools
generateWithGeminiC

Generate content with Gemini 2.5 Pro Experimental (beta API)

Inputs: prompt (required), temperature, maxTokens, safeMode, +1 more.

getModelInfoB

Get information about the Gemini model being used

Inputs: no named parameters.

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections.

Loading tool schema changelog…

    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

    B3/5.0

    Scored across 2 tools

    Disambiguation5/5

    The two tools have completely distinct purposes: generateWithGemini is for content generation, while getModelInfo is for retrieving model metadata. There is no overlap in functionality, making it impossible to confuse them.

    Naming Consistency4/5

    Both tools use camelCase naming, which is consistent. However, generateWithGemini uses a verb+preposition+noun pattern, while getModelInfo uses verb+noun, creating a minor deviation in structure.

    Tool Count3/5

    With only 2 tools, the server feels thin for a Gemini API server, as it lacks operations like listing models, managing conversations, or handling multimodal inputs. The count is borderline for the apparent scope of interacting with a generative AI model.

    Completeness2/5

    The tool surface is severely incomplete for a Gemini API server. It only covers content generation and model info, missing essential operations such as chat/completion management, file uploads for multimodal inputs, or configuration settings, which will likely cause agent failures in complex workflows.

    Maintenance

    ActivityInactive
    ResponsivenessNo recent community issues

    Resources

    Related MCP Connectors

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