The server provides 4 tools with basic schema definitions and descriptions, but several critical gaps prevent higher scoring. All tools have input schemas with type information and parameter descriptions (good baseline), but descriptions lack depth and context about when to use each tool vs alternatives. Parameter descriptions are present but minimal (averaging ~40-50 chars, below the 72-char baseline). No output schemas are documented anywhere in the codebase, LLMs cannot predict what these tools return. Tool naming follows verb_noun convention (positive), but there is no distinction between the two query-based tools (get_video_clip_from_user_query vs get_video_clip_from_image) in terms of when to use one vs the other. Error handling, idempotency guidance, and permission scopes are entirely absent. The tools appear functional for their domain (video processing) but fall short of production-grade quality that would confidently guide an LLM's selection and use.

• Document output schemas for all tools. Specify the exact fields, types, and structure returned. Example: process_video returns {video_id: string, frames_extracted: number, duration_seconds: number}.
• Expand process_video description to 100-150 chars, stating: 'Process a video file to extract frames and prepare it for searching. Returns video metadata including duration and frame count. Required before calling search tools on this video.'
• Add a prerequisite note to get_video_clip_from_user_query and get_video_clip_from_image: 'Call process_video first if you haven't already processed this video file.'
• Distinguish the two clip-retrieval tools explicitly. Example: get_video_clip_from_user_query for text-based searches ('find the scene with the dog'); get_video_clip_from_image for visual similarity ('find scenes matching this photo'). Add this to both descriptions.
• Add error handling guidance. Example: 'If video_path is invalid, error will indicate: "file not found, check path or call process_video first." If query yields no results, try a simpler query or different tool.'
• Implement tool annotations in the MCP server registration: mark process_video with destructiveHint: true (it modifies the video store); mark the three read tools with readOnlyHint: true.
• Add numeric constraints to parameter descriptions if any accept file sizes, durations, or indices. Example: if user_query has length limits, state: 'user_query: Text description (max 500 chars)'.
• Consider consolidating the two video-search tools with a single tool accepting either 'query' (text) or 'image' (base64), letting the tool detect which is provided, rather than forcing LLM disambiguation.

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

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