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ollama-vision-mcp

by ducnm9
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Python
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    TDQS

    A3.6/5.0

    Scored across 4 tools

    Disambiguation3/5

    describe_image and ask_image both handle arbitrary image analysis, and process_clipboard_image explicitly supports describe/ocr/custom-question tasks, creating overlap. However, the input-source distinction (file vs clipboard) reduces ambiguity, and ocr_image is clearly distinct.

    Naming Consistency4/5

    Three tools follow a consistent verb_noun pattern (describe_image, ocr_image, ask_image), and process_clipboard_image also uses verb_noun but adds a modifier. The naming is mostly consistent and predictable.

    Tool Count4/5

    4 tools is a reasonable size for a vision-focused server, covering core operations without being bloated. It could have been higher if not for the redundancy between clipboard and file-based tools.

    Completeness4/5

    The toolset covers the main vision tasks (description, OCR, custom Q&A) and handles both file and clipboard inputs. Minor gaps like batch processing or image comparison are not essential for the stated purpose.

    Maintenance

    ActivityInactive
    ResponsivenessNo issues

    Resources