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?
Annotations already communicate read-only, idempotent, and non-destructive behavior. The description adds useful context beyond those annotations: the tool is paid, deterministic, returns bounded results, and includes dataset provenance. This is valuable without contradicting any annotations.
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?
Two focused sentences with no filler. The primary use case is front-loaded, and the sentence about return behavior adds necessary detail without redundancy.
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?
The core purpose and return type are described, but with 5 parameters, 0% schema coverage, and no output schema, the definition does not fully equip an agent to call the tool correctly. Missing details about top bounds, field requirements, and metric-specific behavior are notable gaps.
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 must compensate, but it mostly restates metric enum values ('count, sum, average, minimum, or maximum') and the grouping concept. It does not explain top, field, dataset_id, or the dependency between metric and field, leaving important parameter semantics undocumented.
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: deterministic grouped statistics instead of downloading rows and calculating locally. It makes the aggregation focus obvious, though it does not explicitly name sibling tools like query_imported_dataset or sample_imported_dataset as the row-returning alternatives.
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?
It gives a clear usage context: choose this tool when you need grouped statistics and want to avoid local computation. However, it does not explicitly state when not to use it or point to which sibling tools should be used for raw-row retrieval or sampling.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Most tools target clearly distinct actions and states, such as searching, sampling, querying, materializing, or requesting paid access. The main ambiguity is between search_public_datasets and search_discovered_datasets, which both search catalog metadata and differ mainly in scope and materialization-state reporting.
All tool names follow a consistent snake_case verb_noun pattern, using clear verbs like list, search, sample, query, request, get, and aggregate. The naming makes the action and subject predictable across the entire set.
With 14 tools, the server covers distinct stages of a coherent workflow: discovery, materialization, status polling, sampling, querying, aggregation, coverage checking, and paid access. Each tool addresses a meaningful step without excessive redundancy.
The tool surface covers the full discovery-to-paid-query lifecycle for imported government datasets and includes dedicated Federal Register access. Minor gaps exist, such as no explicit dataset-detail or payment-status tool, but agents can work around these using list/search and request_paid_access.