This MCP server presents significant definition quality gaps. While all 5 tools have explicit names, descriptions, and input schemas visible in the source code, the descriptions are minimal (averaging ~80 chars), parameters lack depth and context, and there is no documented output schema structure. Error handling is not evident in the tool definitions. The schema quality varies: tools like `recommend-parameters` and `debug-pipeline-step` use enums appropriately, but descriptions do not explain WHEN to use each tool, what dependencies exist, or how to interpret results. The server is clearly domain-specific (ghost-mannequin photo pipeline), but the tool interface assumes deep user familiarity with the 6-step pipeline architecture rather than explaining it. Per the rubric baseline, average tool description length in A+ tools is 194 chars; these average ~80 chars, placing them in the bottom quartile.

• Expand all tool descriptions to 150 - 250 characters. Add: (1) What the tool does, (2) When to use it instead of similar tools, (3) What it returns, (4) Any prerequisites. Example: 'analyze-garment-quality: Evaluates garment image quality using the pipeline's QA system. Use this after preprocessing to detect segmentation or color issues before rendering. Returns quality score (0-100) and list of detected defects. Requires Step 1 (color refinement) to have completed.'
• Add return type schema to each tool. Document expected output fields, types, and example values. Example for get-pipeline-status: 'Returns {current_step: int, queue_size: int, processing_time_ms: number, errors: [string]}'
• For file path parameters (image_path, reference_json, garment_analysis), add explicit format constraints: 'Path to garment image file (JPEG, PNG, max 50MB). Relative paths resolve from /workspace/images/.'
• Describe garment_type and fabric_type enums in detail. Explain why each matters: 'garment_type: Determines model and prompt weights. shirt uses sleeveless template; jacket requires shoulder context.'
• Add error recovery examples to descriptions. E.g., 'If image analysis fails, verify image format, size, and color space (RGB). Call debug-pipeline-step to inspect preprocessing output.'
• Add composition guidance. E.g., in analyze-garment-quality: 'Typically follow with recommend-parameters(garment_type) to get tuned rendering settings for this garment.'
• Define what each pipeline step (0-6) does in debug-pipeline-step description so LLMs understand step numbering: 'Step 0: PreClean, Step 1: Color Refine, Step 2: Prompt Weave, Step 3: Render, Step 4: QA, Step 5: Delivery, Step 6: Feedback.'

Inferred effective spec: <=2025-11-25.

• Relies on Logging (deprecated) - log to stderr or use OpenTelemetry