search_datasets

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

With no annotations provided, the description carries full burden for behavioral disclosure. It mentions the default and max for 'rows', which is useful, but doesn't cover other important aspects like rate limits, authentication requirements, pagination behavior, error handling, or what happens with invalid queries. For a search tool with zero annotation coverage, this leaves significant gaps in understanding its operational behavior.

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 efficiently structured with clear sections (Args, Returns), uses bullet-like formatting, and contains no redundant information. Every sentence adds value: the purpose statement, parameter explanations with examples, and return value description. It's appropriately sized and front-loaded with the core functionality.

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 moderate complexity (search with two parameters), no annotations, and the presence of an output schema (implied by 'Returns' statement), the description is reasonably complete. It covers purpose, parameters with semantics, and return values. However, it could benefit from more behavioral context (like rate limits or error cases) since annotations are absent.

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 description adds meaningful context beyond the input schema, which has 0% description coverage. It explains that 'query' accepts search terms with examples ('climate', 'census', 'health'), and specifies that 'rows' has a default of 10 and max of 50. This compensates well for the schema's lack of descriptions, though it doesn't detail parameter constraints beyond the max for 'rows'.

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 the tool's purpose: 'Search for datasets on Data.gov' with a specific verb ('search') and resource ('datasets'). It distinguishes from siblings like 'get_dataset_info' (which retrieves specific dataset details) and 'query_datagov' (which might be broader). However, it doesn't explicitly differentiate from 'search_eu_datasets' (which searches a different platform).

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 implies usage through the context of searching Data.gov datasets, but doesn't explicitly state when to use this tool versus alternatives like 'search_eu_datasets' (for EU data) or 'query_datagov' (which might have different functionality). It provides basic parameter guidance but lacks explicit when/when-not scenarios or named alternatives.

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