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Hydra Vision MCP

by carlory
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    • vision-mcp

      knownothing20
      A
      license
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      quality
      D
      maintenance
      Enables AI agents to analyze images using any OpenAI-compatible vision API, providing tools for image analysis, OCR, error diagnosis, diagram understanding, and chart analysis.
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    • my-own-vision-mcp

      vectorequa
      A
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      Not graded
      quality
      A
      maintenance
      Enables text-only AI agents to see images on demand by calling any OpenAI-compatible vision API for OCR, image analysis, structured extraction, image comparison, and GUI screenshot-to-accessibility-tree conversion.
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    • Luma MCP

      Collins1150
      A
      license
      Not graded
      quality
      A
      maintenance
      Enables AI coding models without native vision to analyze images such as screenshots, error messages, UI layouts, and documents through a single image_understand tool. Supports multiple vision LLM providers including GLM-4.6V, DeepSeek-OCR, Qwen3-VL-Flash, Doubao-Seed-1.6, Hunyuan-Vision, and any OpenAI-compatible endpoint.
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    • llm-vision-mcp

      KuaaMU
      A
      license
      A
      quality
      A
      maintenance
      Gives text-only LLM coding agents vision by routing images to a multimodal model and returning detailed textual descriptions. Supports local files, URLs, clipboard, base64, raw bytes, and multiple providers like OpenAI, Anthropic, and Gemini.
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    Deploy Server
    Shareable, User-Owned Context BackendPeople, applications, and agents read, write, and coordinate together in one context lake. Reach it over MCP, a CLI, a Python SDK, or REST.fulcradynamics.comAd

    TDQS

    A4.2/5.0

    Scored across 7 tools

    Disambiguation5/5

    Each tool targets a distinct image type or task (UI conversion, OCR, error diagnosis, technical diagrams, data viz, UI diff, general fallback) and descriptions explicitly state boundaries with 'Do NOT use for' exclusions, so an agent can easily select the right tool. The fallback is clearly defined as a last resort, preventing overlap.

    Naming Consistency3/5

    All names use snake_case, but 5 of 7 follow a verb_noun pattern while 'ui_to_artifact' and 'ui_diff_check' start with a noun/adjective and use non-verb constructions. This mixed convention is readable but not fully consistent.

    Tool Count5/5

    7 tools is well-scoped for an image analysis server, with six specialized tools covering common visual tasks plus one explicit fallback. Each tool has a clear purpose and no tool feels redundant.

    Completeness4/5

    The surface covers major screenshot and diagram analysis needs (UI conversion, OCR, error diagnosis, technical diagrams, data viz, UI diff) and includes a general fallback for uncovered cases. Minor gaps exist, such as structured data extraction from charts or non-UI image comparison, but agents can work around these via the fallback.

    Maintenance

    ActivityMaintained
    ResponsivenessNo issues

    Resources