Seattle Data Describe Dataset

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

Annotations already cover the safety profile (readOnlyHint, openWorldHint), so the description earns credit for adding latency and failure behavior: 'Typically 1-3 s; fails with query_timeout after 30 s.' It also flags a non-obvious data caveat (place values vs labels, areas spanning multiple values), which is genuinely beyond structured fields.

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?

Front-loads the payoff, then usage rules, then an example and a performance note. Dense but every line carries decision-relevant information; the enumerated return list is a touch verbose but justified for a metadata 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?

An output schema exists, so return formatting need not be explained. The description supplies everything an agent needs to decide whether to call it, choose detail, and interpret place fields, matching the tool's complexity.

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?

Schema coverage is 100%, so the baseline is 3, but the description adds real decision semantics: when to set detail="full" (area questions) and how to use place fields (filter on value, not label). It also supplies a concrete example call.

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?

States a specific verb (decide whether/how to query) and enumerates exactly what the tool returns: columns, live row count, data_coverage, place fields with reliability, and geo columns. The mention of querying clearly separates it from seattle_data_search_datasets and seattle_data_get_reference.

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?

Gives a clear trigger ('Call before querying a dataset whose columns you do not know') and conditional guidance ('For area questions, use detail="full"'), plus a field-filtering rule. It stops short of explicitly naming seattle_data_query_dataset as the next step or stating when this call can be skipped.

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