aggregate_imported_datasetUse this paid read-only tool for deterministic grouped statistics instead of downloading rows and calculating locally. Returns bounded count, sum, average, minimum, or maximum groups with dataset provenance.
| Name | Required | Description | Default |
|---|---|---|---|
| top | No | ||
| field | No | ||
| metric | No | ||
| group_by | Yes | ||
| dataset_id | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description adds meaningful behavioral context beyond the annotations: it is a 'paid' tool, read-only, deterministic, returns bounded results, and includes dataset provenance. It does not contradict the readOnlyHint or idempotentHint annotations, and the extra details help the agent understand cost and output characteristics.
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 a single, well-structured sentence that front-loads the tool's paid read-only nature and then states the purpose and return characteristics. Every clause earns its place; there is no filler or repetition of schema details.
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 paid tool with no output schema, the description gives a reasonable overview of return shape (bounded groups, stats, provenance) but omits important semantic details like how `top` limits results, when `field` is needed vs. optional, and what the output structure actually looks like. Given the five parameters, this is a noticeable but not severe gap.
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 0% schema description coverage, the description must carry parameter meaning. It explains the metric options (count, sum, average, minimum, maximum) and implies group_by, but it leaves `field` and `top` semantically unexplained. This is partial compensation for the schema's lack of descriptions.
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 a specific action ('aggregate an official dataset') and the resource, with a precise purpose: deterministic grouped statistics instead of local computation. It distinguishes the tool from the general idea of row-level downloading, but it does not explicitly name or differentiate against siblings like query_imported_dataset or sample_imported_dataset.
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 tells the agent when to use the tool: for deterministic grouped statistics rather than downloading rows and calculating locally. It gives clear context for the main use case, though it does not name alternative sibling tools or provide explicit when-not-to-use conditions.
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.