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

No annotations are provided, so the description carries the behavioral disclosure burden. It states that the tool is free to call and not counted against the free tier, and it details exactly what information will be returned across datasets. This is sufficient for a read-only freshness check and contains no contradictions.

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 two sentences with no filler. The first sentence front-loads the intended use case, and the second condenses the full return summary and cost note. Every sentence earns its place.

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?

With zero parameters and no output schema, the description provides all needed operational context: when to call the tool, what it returns, which datasets it covers, and its cost behavior. An agent can decide to invoke it correctly without additional inference.

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 tool has zero parameters, so the empty schema is fully self-describing and there are no parameter ambiguities. The description adds useful context by explaining that results are organized per dataset, which helps the agent interpret the output even without an output 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 the tool's purpose: checking data freshness before quoting figures or when an as_of date looks old. It enumerates the specific return values per dataset, which distinguishes it functionally from the sibling data-query tools even without naming them.

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 gives explicit triggering conditions: use it when the user cares about data currency or when another tool returns a stale-looking as_of date. It does not explicitly state when not to use it or name alternative tools, but the context is clear enough to guide selection.

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