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MCP Server for VLM - A Model Context Protocol server providing vision/video analysis, configurable with any model provider (Chat Completions / Responses / Anthropic).
All 8 tools have basic descriptions and input schemas with type definitions, but quality is inconsistent. Tool names lack action verbs (ui-to-artifact, text-extraction, error-diagnosis are noun-based rather than verb-based like 'extract_text_from_image'). Descriptions are generic and lack WHEN/WHY guidance. Parameter descriptions exist but are minimal (10-30 chars). No output schemas documented. No error handling guidance. No tool annotations (readOnlyHint, etc.). Schemas present but sparse, missing constraints, enums, and validation rules. This is typical community-server quality: functional but not LLM-optimized.
Analyzes data visualizations and extracts meaningful insights, trends, patterns, and actionable recommendations
Analyzes diagrams and provides understanding of their structure and meaning
Analyzes error screenshots and provides diagnostic insights
Performs general-purpose analysis on any image
Extracts text content from images using vision analysis
Compares two UI screenshots and identifies differences
Converts UI screenshots or designs into artifact code/markup
Tool names lack action verbs. All 8 tools use noun-based names (ui-to-artifact, text-extraction, error-diagnosis) instead of verb_noun pattern (extract_text, analyze_ui, diagnose_error). LLMs rely on verb-first names to infer intent before reading descriptions.
No output schemas documented. Tools return vision analysis results but no structured schema is visible for what fields the LLM should expect. This forces LLMs to parse unstructured responses and breaks downstream tool chaining.
Inferred effective spec: <=2025-11-25.
| Scored | Grade | Overall | Spec posture | Rubric |
|---|---|---|---|---|
| 2026-09-23 | F | 32 | <=2025-11-25 | v2 |
Analyzes video content and provides insights about its structure and meaning
Parameter descriptions are minimal (10-30 chars). 'Path to image file or URL of the UI screenshot' and 'User's specific request for the UI analysis' lack guidance on format constraints, valid ranges, or when to use each parameter. Descriptions should be 50-200 chars with actionable detail.
No error handling guidance. Tools do not document what errors can occur, whether they are retryable, or what the LLM should do next. A failed image analysis returns nothing actionable to guide recovery.
No tool annotations. Tools lack readOnlyHint, destructiveHint, or idempotentHint metadata. All 8 tools are read-only (Risk: READ_ONLY) but this is not declared in the tool definition, forcing LLMs to infer safety from descriptions.