Server has 5 tools with basic schemas and descriptions, but significant gaps limit production readiness. All tools have descriptions (10-50 chars, below the 50-200 char baseline for LLM optimization) and input schemas with Zod validation. However, descriptions lack context about when to use each tool, prerequisites, and consequences of state-changing operations. Parameter descriptions are minimal (e.g., 'Playlist ID' vs. 'The unique YouTube playlist identifier'). No output schemas documented. Error handling is absent, tools return success messages but no guidance on failure modes or recovery. Destructive tool (remove_video_from_playlist) lacks confirmation pattern. No pagination support despite potential for large result sets.

• Expand tool descriptions to 50-200 chars. Example: 'list_playlists' → 'Retrieve all YouTube playlists owned by the authenticated user. Returns playlist ID, title, video count, and description. Call this first to discover available playlists before querying videos.'
• Add format and constraint guidance to parameter descriptions. Example: 'playlist_id' → 'YouTube playlist ID (26 alphanumeric chars, typically starts with PLxx). Obtain from list_playlists() or from a YouTube URL.'
• Document output schemas with typed fields. Example: list_playlists returns [{id: string, title: string, videoCount: number, description: string}]. This enables LLMs to extract IDs for downstream tool calls.
• Implement confirmation pattern for remove_video_from_playlist. Add a dry_run parameter or require explicit confirmation before deletion. Return structured error responses with actionable recovery hints.
• Add pagination support to get_playlist_videos. Accept limit (1-50, default 20) and pageToken parameters. Return total count and next_cursor so agents can fetch large playlists without context explosion.