A
licenseNot graded
qualityC
maintenanceEnables MCP clients to gain vision capabilities by analyzing images, extracting text, and comparing images through any OpenAI-compatible vision endpoint.
27 npm
MIT
visionMCP
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TDQS
A3.5/5.0
Scored across 5 tools
Disambiguation5/5
Each tool targets a clearly distinct task: general description, specific Q&A, OCR, image comparison, and server status. An agent can easily select the right one based on the user's intent, with no meaningful overlap in scope.
Naming Consistency4/5
Four tools follow a verb_noun pattern (describe_image, ask_about_image, extract_text, compare_images) in snake_case. However, server_status deviates as a noun_noun, and compare_images uses plural while others are singular, slightly breaking the pattern.
Tool Count5/5
With only 5 tools, the server is well-scoped and each tool serves a necessary function. This is within the ideal range of 3-15 for a focused vision understanding server.
Completeness4/5
Core vision understanding workflows are covered: describing, asking questions, OCR, comparison, and status checks. Minor missing features like explicit object detection or metadata retrieval are easily approximated via ask_about_image, so no critical dead ends.
Maintenance
ActivitySlowing
ResponsivenessNo issues