A
licenseA
qualityD
maintenanceEnables non-vision LLMs to analyze images via any OpenAI-compatible vision API. Hardened against truncation, empty responses, and timeouts for reliable analysis.
1
23 npm
MIT
mcp-six-eyes
by RimunAce
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TDQS
A4.1/5.0
Scored across 10 tools
Disambiguation4/5
Most tools have clearly distinct purposes: general Q&A, description, OCR, comparison, UI inspection, chart reading, diagram explanation, and structured extraction. Some overlap exists between analyze_image, describe_image, and refer_images, but their descriptions clarify the intended use cases, so agent confusion is unlikely.
Naming Consistency4/5
The naming convention is largely verb_noun (analyze_image, compare_images, inspect_ui, read_chart, explain_diagram, extract_from_images). Minor deviations include vision_status (noun_noun) and ocr_image (acronym as verb), but the pattern is readable and predictable overall.
Tool Count5/5
With 10 tools, the server is well-scoped for an image analysis domain. Each tool addresses a distinct need, and the count is within the ideal 3-15 range for a focused MCP server.
Completeness5/5
The tool surface covers general image Q&A, detailed description, OCR, comparison, labeled references, UI inspection, chart reading, diagram explanation, and structured data extraction—no obvious dead ends or missing essential operations for an image understanding server.
Maintenance
ActivityStale
ResponsivenessNo issues