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MCP Visual Language

by lengbone

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    • A
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      Provides image understanding and OCR via GLM-4.6V-Flash, supporting URL, base64, and local file inputs. Enables AI assistants to analyze images and extract text from screenshots, documents, and more.
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    • A
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      Grants image-understanding capabilities to AI coding assistants that lack native vision by routing screenshots, error messages, UI mockups and documents through a single unified analysis tool. Supports multiple vision model providers such as GLM, DeepSeek-OCR, Qwen-VL, Doubao and Hunyuan, with automatic multi-crop, compression and retry handling for text-dense images.
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    • F
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      1
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    • A
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      quality
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      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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    • A
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    TDQS

    B3.2/5.0

    Scored across 1 tool

    Disambiguation5/5

    With only one tool, there is no possibility of ambiguity or overlap between tools. The tool's purpose is clearly defined as analyzing images from various sources, leaving no room for confusion or misselection.

    Naming Consistency5/5

    Since there is only one tool, naming consistency is inherently perfect. The tool name 'auto_analyze_image' follows a clear verb_noun pattern, and there are no other tools to compare it against for inconsistency.

    Tool Count2/5

    A single tool is generally too few for most server purposes, as it limits functionality and flexibility. While the tool itself might be useful, the server's scope appears thin, lacking complementary operations like image editing, filtering, or metadata extraction that would enhance coherence.

    Completeness2/5

    The server is severely incomplete for a visual language domain. It only provides analysis, missing essential operations such as image generation, transformation, annotation, or comparison. This gap will likely cause agent failures when broader image-related tasks are required.

    Maintenance

    ActivityInactive
    ResponsivenessNo issues