dataset_status

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

No annotations are provided, so the description must disclose behavioral traits. It lists the content (freshness, stale states, license, raw endpoints) but does not explicitly state whether this is a read-only, non-mutating operation, nor does it mention any costs, latency, or side effects. While 'status' implies safety, the description carries only partial burden—it lacks explicit reassurance or caveats. With no annotations, a 3 is appropriate.

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?

Two concise sentences with no fluff. The first sentence front-loads the core content, naming the specific data points. The second adds a practical usage hint. Every word earns its place, and the description is appropriately sized for a simple status tool.

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?

This tool has no output schema, so the description must explain what is returned. It lists the key categories: rebuilt time, stale states, license, and raw endpoints. It also gives a usage scenario ('before quoting a number') to help the agent understand when it adds value. For a zero-parameter metadata tool, this is complete—nothing an agent needs to know to call it correctly is missing.

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 has zero parameters, so the description is the sole source of semantic meaning. It clearly explains that the tool provides dataset-level metadata rather than taking inputs, and it lists the categories of information returned. This fully compensates for the empty schema, matching the baseline of 4 for a no-parameter tool.

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 provides 'Freshness and coverage of the dataset itself' and enumerates specific aspects (last rebuilt, stale states, license, raw endpoints). This is a specific noun phrase that distinguishes it from sibling tools like search_layoff_notices or state_layoff_totals, which operate on record-level data. No ambiguity about what the tool returns.

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?

Provides explicit when-to-use context: 'Call this before quoting a number so you can attribute it correctly.' This tells the agent the appropriate timing for invocation. It does not mention alternatives or exclusions, but for a metadata tool that is mainly a pre-call context check, this is sufficient. Siblings are clearly about data retrieval, so no conflict.

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