A
licenseA
qualityA
maintenanceEnables converting images (JPEG, PNG, GIF, WebP) into text descriptions using OpenAI-compatible vision models, with support for both local files and URLs.
2
15 npm
MIT
Computer Vision MCP Server
by samhains
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TDQS
C2/5.0
Scored across 4 tools
Disambiguation5/5
Each tool has a clearly distinct purpose: alt_text generates descriptive text for accessibility, caption_image creates a general caption, dense_caption provides detailed region-specific captions, and image_metadata extracts technical data. There is no overlap in functionality, making tool selection unambiguous.
Naming Consistency5/5
All tool names follow a consistent snake_case pattern with descriptive noun-based naming (alt_text, caption_image, dense_caption, image_metadata). The naming is uniform and predictable across all four tools.
Tool Count4/5
With 4 tools, the count is reasonable for a computer vision server, covering key image analysis tasks. It is slightly lean but well-scoped, as each tool addresses a distinct aspect of image processing without redundancy.
Completeness3/5
The tools cover descriptive and metadata extraction tasks well, but there are notable gaps in core computer vision operations like object detection, image classification, or segmentation. The surface is incomplete for a full computer vision workflow, though the provided tools are coherent within their subset.
Maintenance
ActivityInactive
ResponsivenessNo issues