search_datasets

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

The description explains that the tool returns a list of DatasetSummary objects ranked by relevance and that datasets are curated. It also provides example output structure. However, it does not address behavior like rate limiting or error handling, but for a search tool, the provided detail is sufficient.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise yet comprehensive: it starts with a clear purpose, provides usage guidance, includes illustrative examples, and states the return format. Every sentence adds value, and the structure is logical and easy to scan.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity (2 parameters, output schema exists), the description covers all essential aspects: purpose, when to use, return format, and examples. The output schema handles detailed return field definitions, so the description is complete. No omissions for typical usage.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema already covers both parameters with descriptions and examples (100% coverage). The description adds value by explaining that the search is fuzzy, results are ranked by relevance, and the curated nature of the datasets. This context enriches the parameter meaning beyond the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states that the tool performs fuzzy-search on the 7 curated AEMO NEM datasets, and specifies it is for use when exact dataset_id is unknown. This distinguishes it from siblings like describe_dataset (which requires a known ID) and list_curated (which lists all curated datasets). The purpose is specific and actionable.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly advises using this tool when the exact dataset_id is unknown, and mentions that curated datasets cover ~95% of typical NEM queries. This provides clear context for when to use this tool versus alternatives. Examples further illustrate usage scenarios.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.