A
licenseNot graded
qualityC
maintenanceProvides structured visual analysis tools for coding agents, enabling UI analysis, screenshot comparison, OCR, and crop-based refinement through VLM.
MIT
lewm-mcp
by Gonzih
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TDQS
A3.8/5.0
Scored across 6 tools
Disambiguation3/5
Some tools have overlapping purposes: analyze_screenshot with a previous frame and compare_states both compare two screenshots and return similar metrics. Similarly, analyze_video and run_surprise_detection both process videos and compute surprise scores, though run_surprise_detection is a higher-level pipeline.
Naming Consistency5/5
All tool names follow a consistent verb_noun pattern (e.g., load_model, analyze_screenshot, get_model_status). The naming is predictable and easy to infer.
Tool Count5/5
Six tools is a well-scoped count for a surprise-detection server. Each tool addresses a distinct part of the workflow (model management, single-image analysis, pairwise comparison, video analysis, full pipeline) without redundancy.
Completeness4/5
The server covers the core workflow: load model, check status, analyze images/videos, compare states, and run a full detection pipeline. Minor gaps exist, such as no explicit way to unload the model or customize the loaded checkpoint beyond defaults, but these are not critical.
Maintenance
ActivityInactive
ResponsivenessNo issues