All 12 tools have basic verb-noun naming (get_*, search_*) and descriptions present. However, descriptions are minimal (10-50 chars), falling below the 50-200 char LLM-optimized baseline. Input schemas are visible and properly typed, but lack depth: no enums for constrained values (e.g., query_type accepts 'Latest'|'Top' but not declared as enum), no min/max bounds on numeric parameters (count, cursor pagination), and no guidance on parameter relationships or dependencies. Output schemas are not documented, LLMs cannot predict response structure for chaining. Error handling is absent from tool definitions. Parameter descriptions are generic ('The Twitter username') without format hints or constraints. No tool declares idempotency, permissions, or rate-limit guidance.

• Expand tool descriptions to 50-200 chars. Include WHAT the tool does, WHEN to use it, and what it returns. E.g., 'Get a tweet by ID. Returns tweet text, author, engagement metrics (likes, retweets, replies). Use this to fetch a specific tweet when you have its ID; use search_tweets to find tweets by keyword.'
• Document output schemas for all tools. Specify which fields are returned (e.g., tweet: {id, text, author_id, created_at, public_metrics: {like_count, retweet_count, reply_count}}). This enables LLMs to chain tools and extract data correctly.
• Add min/max bounds to numeric parameters. E.g., count: integer, minimum 1, maximum 100, default 10. This prevents LLMs from requesting unreasonable result sets.
• Convert query_type to an enum: {type: 'string', enum: ['Latest', 'Top']}. Enums are self-documenting and prevent hallucinated values.
• Add error recovery hints to descriptions. E.g., 'If user not found, try search_users() with a partial name. If rate limited (429), wait 15 seconds before retrying.' This guides LLM behavior on failures.