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 readOnlyHint=true and idempotentHint=true, so the description does not need to restate read-only behavior. It adds valuable behavioral context beyond annotations: it is a paid tool, returns at most 100 rows, has bounded pagination, and supports one exact-match filter. This goes beyond what the annotations provide.
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 sentences, front-loaded with the critical usage gate and paid read-only nature, followed by a compact enumeration of behavior. Every phrase earns its place and there is no redundancy with annotations or schema.
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 read-only query tool with 5 parameters and no output schema, the description gives the essential selection and invocation context: when to use it, row limit, pagination bound, filter behavior, and provenance/freshness. It could mention output shape or error behavior, but the core decision-making information is present.
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 carries the burden. It adds meaning for filter_field/filter_value ('one exact-match filter') and for top/skip ('up to 100', 'bounded pagination'), but dataset_id is not explicitly explained and skip semantics are only implied. The description partially compensates for the schema gap but not fully.
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 names a specific verb (query), resource (imported dataset), and scope: returns up to 100 normalized rows from a published snapshot. It also distinguishes itself from sample_imported_dataset by saying it runs after that tool confirms suitability, and from aggregate_imported_dataset by describing row-level output.
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 explicitly tells the agent to use this tool after sample_imported_dataset confirms the data is suitable, which is clear contextual guidance. It does not explicitly list exclusions or when to prefer aggregate_imported_dataset, but the usage context is strong enough for a 4.
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.