Pixazo MCP server provides 8 well-structured tools with explicit Zod schemas, descriptions, and tool annotations (readOnlyHint, destructiveHint). However, several definition quality gaps prevent a higher score: (1) Output schemas are NOT documented, the rubric requires 'Document the output schema' but the tools return generic 'content' objects with no typed schema specification; (2) Parameter descriptions lack critical constraint details (ranges, formats, patterns) that would prevent LLM hallucination; (3) Error handling is minimal, no recovery guidance, no actionable error messages documented; (4) Some tools return generic text responses instead of structured data; (5) Tools accept image/video URLs but lack validation guidance. The tools follow basic naming (verb_noun), have descriptions (100-200 chars, within baseline range), and explicit input schemas with enums. However, the lack of output schema documentation and minimal error handling guidance are significant gaps for production use. The model_enum list documents all available models clearly, which is a strength. Tool composition is clean (each tool has one responsibility), though some parameter interdependencies are not fully documented (e.g., when to use image_url vs video_url vs first_frame in generate_video).

• Document output schema for all generation tools. Example: 'Returns { content: [{ type: "text", text: "<generation_success_message_with_url>" }] }, or on error: { content: [{ type: "text", text: "<error_description>" }] }'.
• Add explicit min/max constraints to numeric parameters. E.g., 'duration: number between 30 and 180 seconds' for music generation.
• Convert parameter examples to enum constraints. E.g., 'resolution' should be an enum (720P | 1080P | 2K | 4K) instead of descriptive text.
• Document error scenarios and recovery steps for each tool. E.g., 'If API returns 429 (rate limit), retry after 60 seconds. If image URL fails (4xx), verify URL is accessible and in JPG/PNG format.'
• Clarify parameter interdependencies in generate_video: document which models accept which input types (prompt-only, image-to-video, video-to-video, frame-to-video) and mark mutually exclusive parameters.
• Add input validation guidance to URL parameters: specify accepted formats (JPEG, PNG, MP4), max file sizes, and what happens if validation fails.
• Encapsulate API contract quirks (image_url vs image field mapping) entirely in the tool implementation; do not require LLM awareness of model-specific field name changes.
• Consider pagination for list_models if the list grows beyond 50 items; document current result count and provide a limit parameter.