Noteboxd MCP demonstrates strong definition quality with 17 well-structured tools, all featuring proper verb-noun naming (search, get_, list_), complete input schemas with Zod validation, and consistent descriptions. All tools are READ_ONLY with proper annotations. However, output schemas are not explicitly documented in the code, and some parameter descriptions lack actionable constraints (e.g., format specifications). Tool descriptions are generally adequate (50-150 chars) but could be more prescriptive about when to use each tool vs. similar alternatives. No security issues detected (read-only operations only). Composition is sound, each tool has a single responsibility, and fragrance lookup chains are well-designed (search → get_fragrance → enrich_fragrance). The batch_get_fragrances and enrich_fragrance tools show good awareness of agent efficiency patterns.

• Document output schemas for all 17 tools. For each tool, add TypeScript interfaces or JSDoc comments specifying the response structure. Example: get_fragrance returns { id, slug, name, brand, notes: [{id, name}], accords, perfumer, communityScore, ... }.
• Enhance error handling in fail() function. Categorize errors: a 400 for invalid slug should return 'Invalid brand slug. Slugs must be lowercase kebab-case (e.g., tom-ford). Try search() to find the correct slug.' This guides the agent toward recovery.
• Add format specifications to slug/id parameters. Update descriptions: 'Brand slug, lowercase with hyphens (e.g., tom-ford, maison-francis-kurkdjian)'.
• Clarify tool selection logic. Expand search_fragrances description: 'Use for multi-faceted filtering by brand, note, family, or price. Requires at least one filter. For open keyword lookups without filters, use search() instead.'
• Standardize pagination. Choose cursor-based (preferred for mutable data) or offset/page (simpler for read-only APIs). Document whether each tool's pagination is exhaustive and what the nextCursor/next_page field name is.
• Add composed tool examples in README. Show common agent workflows: 'Search for fragrances → enrich_fragrance → compare_fragrances' to guide LLM composition.
• Include result limits in descriptions. E.g., 'Trending returns up to 50 items. Results are ordered by wearing count (last 24 hours) and refreshed hourly.' This sets agent expectations.
• Return chaining IDs in all responses. If get_fragrance returns brand and perfumer data, ensure brand_id and perfumer_id are included so agents can call follow-up tools without extra lookups.