A
licenseNot graded
qualityB
maintenanceProvides image understanding capabilities via local OCR and cloud VLM, enabling text extraction and visual description through MCP.
17 npm
2
MIT
FusionVisionMCP
by Whoawhen
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TDQS
A4.5/5.0
Scored across 11 tools
Disambiguation3/5
Most tools have clear boundaries (caption vs detect vs count vs spatial relations), but ocr and query_image directly contradict each other on which should read stylized/watermark text, and caption adds a third text-related caveat. An agent following the descriptions could route a text-reading request to the wrong model.
Naming Consistency3/5
Names are uniformly lowercase snake_case and mostly readable, but conventions are mixed: verb_noun (detect_objects, score_aesthetics) sits alongside bare nouns (ocr, caption), an adjective_noun (spatial_relations), and a multi-part modifier (dense_region_caption). No single predictable pattern emerges.
Tool Count5/5
11 tools is well within the ideal range and each fills a distinct role in the vision-understanding domain: OCR, captioning, VQA, detection, counting, spatial geometry, aesthetics, batch processing, and a raw escape hatch. The count feels deliberately scoped rather than padded.
Completeness5/5
The surface covers the major image-understanding operations—transcribe, describe, locate, count, measure relationships, and score aesthetics—and batch_analyze_images plus the raw process escape hatch close most workflow gaps. No obvious dead end or missing lifecycle step stands out for this domain.
Maintenance
ActivityMaintained
ResponsivenessNo issues