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Luma MCP

Luma MCP

by JochenYang
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    Related Servers

    • Luma MCP

      mariaa481
      A
      license
      Not graded
      quality
      A
      maintenance
      A multi-model vision MCP server that gives image understanding to AI coding assistants lacking native vision, letting them analyze screenshots, error messages, UIs, and documents through a single image_understand tool. It routes requests to major vision LLM providers such as GLM, DeepSeek-OCR, Qwen-VL, Doubao, and Hunyuan, with automatic image preprocessing, cropping, and retries.
      1,048 npm
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    • Luma MCP

      sChauhan9915
      A
      license
      Not graded
      quality
      A
      maintenance
      A multi-model vision MCP server that gives image understanding to AI coding models lacking native vision, letting them analyze screenshots, error images, UIs, documents, and OCR content through a single unified tool. It supports several mainstream vision LLM providers plus any OpenAI-compatible endpoint, with automatic image cropping, compression, and retry handling.
      1,048 npm
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    • glm-vision

      yblt
      F
      license
      Not graded
      quality
      D
      maintenance
      MCP server that gives text-only models vision capabilities via free GLM vision models, supporting image description, OCR, chart/document analysis, and grounding with automatic model fallback.
      1
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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

    A4.4/5.0

    Scored across 1 tool

    Disambiguation5/5

    Only one tool exists, so there is no possibility of misselection between tools. The tool's purpose is clearly defined with explicit call conditions and task types.

    Naming Consistency5/5

    The server has a single tool named 'image_understand' in snake_case. With only one tool, there is no naming pattern to conflict; the name is descriptive and consistent with a verb-adjacent structure.

    Tool Count3/5

    The server exposes exactly one tool. While the tool is comprehensive and well-designed, a single tool feels thin for an MCP server per the calibration guidelines, placing it in the borderline category.

    Completeness5/5

    The image_understand tool covers a broad range of use cases (general understanding, OCR, UI analysis, debugging, and description) via its task_type parameter. It provides a complete lifecycle for image understanding, with no obvious missing operations within its stated scope.

    Maintenance

    ActivityInactive
    ResponsivenessResponsive

    Resources