Three tools with complete schemas and descriptions, but significant gaps in parameter documentation and output schema clarity. Tool names follow verb_noun convention (search_, dataset_, query). Descriptions are present (avg 156 chars) but lack actionable guidance on when to use each tool vs. alternatives. Parameters have types but many lack descriptions or constraints. No output schema documented. Error handling is minimal. Composition is sound (search → info → query workflow), but parameter relationships underdocumented.

• Document output schemas for all three tools. Example for search_datasets: 'Returns {datasets: [{dataset_id, title, theme, record_count}], total_count}'. This enables LLMs to chain calls and extract IDs.
• Add range and format constraints to parameter descriptions. E.g., 'limit: Max datasets to return (1-100, default 20)' and 'where: ODSQL WHERE clause (e.g., year >= 2020 AND city = "Paris")'. Rubric: describe expected format, range, and allowed values in parameter descriptions.
• Add recovery hints to error messages. When dataset_id is invalid, return: 'Dataset not found. Call search_datasets() with a keyword to find valid dataset IDs.' This guides the LLM to self-correct.
• Clarify parameter relationships in descriptions. For query tool: 'where and query are mutually exclusive; where takes precedence if both provided.' Rubric: document undocumented dependencies.
• Add tool selection guidance to descriptions. E.g., dataset_info: 'Call this after search_datasets() to learn column names before querying.' This prevents wasted calls and guides multi-step planning.