sample_imported_datasetUse this free tool to evaluate a query-ready official dataset before paying. Returns three normalized rows, deterministic summaries, freshness, and provenance; use query_imported_dataset only after the sample proves useful.
| Name | Required | Description | Default |
|---|---|---|---|
| dataset_id | Yes | Exact query-ready dataset ID returned by list_imported_datasets. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, idempotentHint, and destructiveHint, so the safety profile is covered. The description adds valuable behavioral detail beyond annotations: it returns three normalized rows, deterministic summaries, freshness, and provenance, and frames sampling as an evaluation-only step.
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 concise, front-loaded with the core value proposition, and uses a second sentence to clarify the follow-up tool. Every sentence earns its place with minimal 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?
For a simple single-parameter, read-only tool with rich annotations, the description is complete. It explains what the output contains, when to use the tool, and what to do next, so an agent has enough context to invoke it correctly.
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 100%, and the dataset_id parameter is well documented as an exact query-ready dataset ID from list_imported_datasets. The description itself does not need to add much about the parameter, so baseline 3 is appropriate.
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 samples a query-ready official dataset for free evaluation before payment, with a specific verb, resource, and tangible outputs. It also names the sibling query_imported_dataset, distinguishing this tool's purpose from that alternative.
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 to use this tool before paying and to use query_imported_dataset only after the sample proves useful, providing clear when-to-use guidance. It does not explicitly exclude specialized siblings like sample_us_federal_register, but the overall context is clear enough for the primary workflow.
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.