Answer a question from verified data

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

The description goes well beyond the minimal annotations by disclosing that answers are computed via real Python in a sandbox, that nothing is model-estimated, that calls take 10-40 seconds, that refusals occur when no series can answer, and that keyless calls return key_required. It also exposes plan-based limits and the series_limit error.

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 dense but every sentence earns its place, covering purpose, behavior, outputs, limits, latency, and authentication. Front-loaded with the core purpose, then structured around key behaviors like CROSS-SOURCE, errors, refusal, and key requirements.

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?

Despite having no output schema, the description enumerates what the caller will receive: computed answer, Plotly chart, Python code, citations, and verification badges. It also covers failure modes, latency, plan limits, and authentication, making the tool callable without guessing.

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?

With 75% schema description coverage, the schema already documents start, end, and series. The description adds useful semantics for question ('natural-language question') and series (cross-source joining, plan limits, series_limit error), though it does not expand on start/end 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 opens with a specific verb and resource: 'ask a natural-language question about the served official statistics.' It distinguishes this tool from data-retrieval siblings by emphasizing computed answers, Plotly charts, code, citations, verification badges, and cross-source joins.

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 makes the intended use clear: use this tool for natural-language analytical questions, especially cross-source comparisons. It does not explicitly name alternatives or state when not to use it, but the 'flagship' framing plus the contrast with raw series retrieval tools gives strong contextual guidance.

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