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Alternatives to plain-sight

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    Related Servers

    • A
      license
      A
      quality
      B
      maintenance
      Zero-dependency MCP server that equips text-only LLMs (e.g., DeepSeek) with vision by converting images to text descriptions via OpenAI-compatible vision APIs, exposing an img2text tool with multi-provider fallback.
      1
      20 npm
      2
      MIT
    • A
      license
      Not graded
      quality
      C
      maintenance
      A local MCP server that gives LLMs eyes for images by performing object detection (YOLOv8) and text recognition (EasyOCR), outputting descriptive statements about objects and text positions without any API key or cloud dependency.
      MIT
    • A
      license
      Not graded
      quality
      C
      maintenance
      An MCP server that enables any LLM to describe images from file paths, URLs, or base64 data by forwarding them to a supported vision provider such as OpenAI, Anthropic, or local Ollama models.
      693 npm
      10
      MIT
    • A
      license
      Not graded
      quality
      C
      maintenance
      MCP server that provides a 'borrowed eye' for text-only LLMs, enabling them to identify and describe local images via the Qwen VL vision model, including face recognition, scene description, OCR, and targeted visual questioning.
      3 npm
      Apache 2.0

    TDQS

    A3.9/5.0

    Scored across 5 tools

    Disambiguation4/5

    Tools have distinct purposes: single image description, batch captioning with sidecars, OCR, status check, and self-test. The only mild overlap is between describe_image and describe_batch, but the sidecar/dataset contract and blocking behavior make them clearly separable.

    Naming Consistency3/5

    Three tools follow verb_noun (describe_image, describe_batch, read_text) while two follow noun_noun with a sight_ prefix (sight_status, sight_selftest). The split is readable but not a single consistent pattern.

    Tool Count5/5

    Five tools is well within the ideal range for a focused image description/OCR server; each tool has a clear role and none feels redundant.

    Completeness4/5

    Core workflows are covered: single and batch description, OCR, status, and self-test. Minor gaps exist, such as no explicit tier selection tool or a way to get batch captions without writing sidecars, but these are workable.

    Maintenance

    ActivityMaintained
    ResponsivenessNo issues