query_datagov

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It states the tool returns 'Raw JSON response from Data.gov API,' which hints at the output format, but fails to cover critical aspects like authentication requirements, rate limits, error handling, or whether it's a read-only or mutating operation. For a raw API query tool, this is a significant gap in transparency.

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 well-structured and front-loaded, with the core purpose stated first, followed by clear sections for 'Args' and 'Returns.' Each sentence earns its place by providing essential information without redundancy, making it efficient and easy to parse.

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 (raw API queries with 2 parameters), lack of annotations, and no output schema, the description is incomplete. It covers basic purpose and parameters but omits behavioral details like authentication, error handling, and usage context. For a tool that interacts with an external API, this leaves significant gaps in understanding for an AI agent.

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 schema description coverage is 0%, so the description must compensate. It adds value by explaining that 'action' is a 'CKAN action' with examples (e.g., 'package_search'), and 'params' are 'Query parameters for the action.' However, it doesn't detail common actions or parameter structures beyond this, leaving gaps in understanding. This partial compensation justifies a baseline score of 3.

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: 'Make a raw query to the Data.gov CKAN API.' It specifies the verb ('query') and resource ('Data.gov CKAN API'), distinguishing it from siblings like 'search_datasets' or 'get_dataset_info' which likely provide higher-level abstractions. However, it doesn't explicitly contrast with these siblings, keeping it at a 4 rather than a 5.

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 provides no guidance on when to use this tool versus alternatives. It doesn't mention scenarios where a raw query is preferred over more specific sibling tools (e.g., 'search_datasets' for dataset searches), nor does it outline prerequisites or exclusions. This lack of contextual direction leaves the agent with minimal usage cues.

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