A
licenseA
qualityD
maintenanceEnables AI agents to analyze images, extract text, compare images, and analyze video through any OpenAI-compatible vision model.
4
166 npm
20
MIT
vision-router-mcp
by wotonger
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TDQS
A4.4/5.0
Scored across 4 tools
Disambiguation5/5
Each tool serves a clearly distinct purpose: listing configurations, providing setup guidance, analyzing images, and testing connections. No two tools overlap in functionality, so an agent can easily select the right one.
Naming Consistency4/5
All tools share the 'vision_' prefix and generally follow a verb_noun pattern (list_models, get_setup_guide, test_connection). 'vision_analyze' is slightly inconsistent as it lacks a noun, but the pattern is still highly predictable and readable.
Tool Count5/5
With 4 tools, the server is tightly scoped to the vision-routing domain. Each tool is essential and there are no redundant or unnecessary additions.
Completeness5/5
The tool surface covers the full lifecycle for a vision router: discovering available models, understanding setup, testing connectivity, and performing analysis. No obvious gaps exist for the stated purpose.
Maintenance
ActivitySlowing
ResponsivenessNo issues