Three tools with clear verb-noun naming (search_datasets, dataset, query_resource) and reasonable descriptions (120-180 chars). All have input schemas with type definitions and required fields properly marked. However, parameter descriptions lack actionable constraints (no enums, ranges, or format specs), output schemas are undocumented, and error handling is minimal. No tool annotations (readOnlyHint, etc.). Descriptions are adequate but generic, they state WHAT but not WHEN or WHY to use each tool. Parameter descriptions are present but sparse (avg ~40 chars vs baseline 72 chars). No guidance on chaining tools or handling failures.

• Add output schema documentation to each tool. Example for search_datasets: 'Returns {results: [{id, name, title, organization, resources: [{resource_id, name, format}]}], count, total}'. This enables LLMs to plan downstream calls.
• Upgrade parameter descriptions with constraints. Replace 'Keyword(s); blank/"*" lists all' with 'Search query (string, 1-500 chars; use "*" to list all datasets)'. Add range/enum info to limit, offset, and q parameters.
• Add readOnlyHint: true to all three tools in the inputSchema. This signals to agents that these tools are safe to call without confirmation.
• Enhance error messages with recovery guidance. Instead of 'Required argument "id" is missing', return 'Required argument "id" is missing. Call search_datasets() first to find a dataset id or name.'
• Add dependency hints to tool descriptions. E.g., 'Call search_datasets() first to find dataset ids, then pass them to dataset() for full metadata, then use resource_ids with query_resource() to fetch rows.'