query_imported_datasetUse this paid read-only tool after sample_imported_dataset confirms the data is suitable. Returns up to 100 normalized rows from a published snapshot with bounded pagination, one exact-match filter, freshness, and provenance.
| Name | Required | Description | Default |
|---|---|---|---|
| top | No | ||
| skip | No | ||
| dataset_id | Yes | ||
| filter_field | No | ||
| filter_value | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare read-only, idempotent, and non-destructive hints. The description adds valuable behavioral details: it is a paid operation, returns up to 100 rows with bounded pagination, and supports only one exact-match filter. It also mentions 'freshness and provenance' which are additional behavioral attributes not covered by annotations. No contradictions exist.
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, concise and well-structured. The first sentence front-loads the usage guideline and the second lists capabilities. No redundant words, each phrase adds value.
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?
For a tool with 5 parameters, no output schema, and no schema descriptions, the description offers a solid overview: it covers usage prerequisite, output characteristics (up to 100 rows, normalized), pagination bounds, filter limit, and data provenance. It doesn't detail error handling or the exact meaning of 'freshness' and 'provenance', but these are minor gaps for an agent to correctly invoke the tool.
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 description coverage is 0%, so the description holds the full burden for parameter meaning. It implies the roles of parameters: 'top' and 'skip' for bounded pagination, 'filter_field' and 'filter_value' for exact-match filter, and 'dataset_id' for the published snapshot. Thus it provides significant semantic context even though it doesn't explicitly name each parameter. It could be more explicit by mapping parameters, but it's good enough.
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 'queries an imported official dataset' with specific capabilities: 'Returns up to 100 normalized rows...' It also differentiates itself from siblings by mentioning 'paid read-only' and the prerequisite of using sample_imported_dataset first. The verb 'query' and the resource 'imported dataset' are specific and unambiguous.
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 explicitly says 'Use this paid read-only tool after sample_imported_dataset confirms the data is suitable,' providing a clear sequential usage guideline. It also implies the alternative usage: if you need to check suitability first, use the sample tool. The paid aspect suggests that free alternatives might exist, though not named explicitly, but the context is strong.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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Most tools map to distinct lifecycle stages—discovery, materialization, sampling, querying, and access—but several discovery tools overlap in purpose. search_public_datasets and search_discovered_datasets both return catalog matches, and list_official_sources and get_coverage_status both describe coverage. The descriptions help separate them, but an agent could still misselect without careful reading.
All tool names follow a consistent snake_case verb_noun pattern, such as search_, list_, get_, request_, sample_, and query_. The noun phrases are descriptive and parallel, making the naming predictable across the entire set.
Fourteen tools is well within the ideal range for a public-data platform and covers discovery, materialization, sampling, querying, aggregation, coverage monitoring, capability requests, and paid access. Each tool has a justified role in the workflow, with no obvious bloat.
The tool surface covers the full data lifecycle: discover sources, search datasets, request materialization, poll status, sample, query, aggregate, and request missing capabilities. It also includes billing access and Federal Register-specific workflows, leaving no obvious dead ends for the stated domain.