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Gemini MCP Server

by georgejeffers
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TypeScript
Hybrid

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    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

    A3.9/5.0

    Scored across 7 tools

    Disambiguation5/5

    Each tool has a clearly distinct purpose with no overlap: chat handles conversations, code_execution runs Python, edit_image modifies single images, edit_image_multi handles multiple images, generate_image creates images, generate_text produces text, and generate_with_search adds search grounding. The descriptions clearly differentiate their functions, making misselection unlikely.

    Naming Consistency5/5

    All tools follow a consistent verb_noun pattern (e.g., chat, code_execution, edit_image, generate_image, generate_text, generate_with_search). The naming is uniform and predictable across all seven tools, with no deviations in style or convention.

    Tool Count5/5

    With 7 tools, the count is well-scoped for a Gemini MCP server, covering core AI functionalities like text generation, image handling, code execution, and chat. Each tool earns its place without feeling excessive or insufficient for the server's purpose.

    Completeness4/5

    The tool surface is nearly complete for a Gemini AI server, covering text generation (with and without search), image generation and editing, code execution, and chat. A minor gap exists in lacking explicit tools for model management or configuration, but core workflows are well-covered and agents can work around this.

    Maintenance

    ActivityInactive
    ResponsivenessNo issues

    Resources