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Alternatives to shadow-vision

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    Related Servers

    • A
      license
      A
      quality
      B
      maintenance
      MCP server that provides vision capabilities to coding agents, enabling them to analyze screenshots, UI mockups, terminal errors, documents, tables, and charts through OpenAI-compatible vision models. Supports local stdio and remote HTTP deployments with structured JSON output and binary upload side channels.
      8
      48 npm
      MIT
    • A
      license
      Not graded
      quality
      D
      maintenance
      MCP server that gives Claude and other clients vision capabilities including screenshot-to-code, OCR, error diagnosis, diagram analysis, data-viz insights, UI diff checks, and video analysis via any OpenAI-compatible vision endpoint.
      20,834 npm
      Apache 2.0
    • A
      license
      Not graded
      quality
      D
      maintenance
      Local vision-capable MCP server that lets AI agents describe screenshots, UI, charts, and photos via vision and OCR tools, with support for multiple providers and automatic fallback.
      5
      MIT
    • 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 of code, errors, UI, and documents or perform OCR. It routes requests through major vision LLM providers and can be shared over local networks via HTTP or Docker.
      1,048 npm
      MIT
    • 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
      MIT

    TDQS

    B3.4/5.0

    Scored across 6 tools

    Disambiguation4/5

    Each tool targets a distinct output modality: description, annotations, OCR text, layout JSON, code reconstruction, and multi-image comparison. Some boundary blur exists between vision_inspect (general description, which may incidentally read text) and vision_ocr, and between vision_layout and vision_reconstruct, but the descriptions clarify the intended scope of each.

    Naming Consistency5/5

    All six tools follow a strict vision_<verb> snake_case pattern (inspect, annotate, ocr, layout, reconstruct, compare). The convention is predictable and readable throughout.

    Tool Count5/5

    Six tools is well-scoped for a vision-analysis server, with each tool earning its place by covering a distinct capability. No redundant or filler tools are present.

    Completeness4/5

    The surface covers the major analysis workflows: description, OCR, annotation detection, layout parsing, code reconstruction, and multi-image comparison. Minor gaps like detecting/returning bounding boxes or image generation/editing exist, but they are outside the apparent analysis-focused purpose.

    Maintenance

    ActivityNo data
    ResponsivenessSyncing