The server defines 35 tools with explicit schemas and descriptions, but quality is inconsistent across the tool set. Strengths: all tools have descriptions (>20 chars), all input schemas are visible with type definitions, verb-noun naming convention is consistently applied, and parameter descriptions are generally present. Weaknesses: descriptions are often generic or vague (e.g., 'Chat sessions with metadata' instead of explaining WHEN to use it), many descriptions lack actionable guidance about prerequisites or next steps, output schemas are not documented, error handling guidance is minimal, and several tools expose multiple responsibilities (e.g., library_add performs both ADD and DISCOVERY in one call; notebook_ask supports both session creation and reuse without clear branching). Tool naming is verb-first and mostly clear, but some parameter choices accept both IDs and URLs without disambiguation (notebook_id vs notebook_url in ~15 tools), forcing LLMs to reason about which to use. No enum constraints are visible for multi-option parameters like 'source_type' (file|url|youtube|drive|text) or 'content_type', these are documented in descriptions but not enforced structurally. Schema inspection of sample tools shows parameters have descriptions but no min/max bounds on numeric types, no pattern constraints on IDs, and no documented defaults. Baseline: 549 production tools average 4 params per tool (observed: 35 tools with 1-10 params each, median ~3.5 ✓); 90% of A+ tools have action verbs (observed: 100% ✓); 100% of A+ tools have docstrings (observed: 100% ✓); but 100% of A+ tool params have descriptions (observed: ~85%, many params like 'days_old' in server_cleanup lack detail about behavior at boundary values). Output schema documentation is the largest gap: tool descriptions state WHAT the tool does but never document the response structure, which violates mxe:response-shaper and mxe:include-chaining-ids patterns.

• Document output schemas for all tools. Create a data dictionary (e.g., 'library_list returns {notebooks: [{id, name, description, url, topics, use_cases, stats}], total_count: int}') in each tool description or in a separate schema appendix. This enables agents to plan chaining and extract nested data without trial-and-error.
• Replace dual notebook_id|notebook_url parameters with a single smart resolver. Internally, accept either format (ID or URL), resolve to the canonical form, and store/return the ID. This reduces LLM reasoning overhead. Alternatively, create separate tools: get_notebook_by_id, get_notebook_by_url.
• Add JSON Schema enum constraints for multi-option string parameters. Convert 'source_type' description 'Type of source: file, url, youtube, drive, or text' into a formal enum: {"enum": ["file", "url", "youtube", "drive", "text"]}. Same for content_type, video_style, presentation_style, etc.
• Add min/max bounds to numeric parameters. E.g., 'limit': {type: integer, minimum: 1, maximum: 100}; 'days_old': {type: integer, minimum: 1, maximum: 3650}. Document in descriptions: 'days_old (1 - 3650, default 30): delete sessions older than N days.'
• Enhance descriptions with actionable guidance. Instead of 'Ask a question to NotebookLM...', write: 'Ask a question grounded on notebook sources with Gemini 2.5. For contextual follow-ups, pass session_id; to create a new session, omit it. If you only have a notebook name, call library_search() first.' This guides multi-step planning.
• Split library_add into separate tools: library_add (URL + optional metadata) and library_discover (auto-populate metadata from URL). This clarifies agent intent and prevents confusion about when auto-discovery is triggered.

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

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