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 readOnly, idempotent, and non-destructive behavior, so the bar for extra disclosure is lower. The description adds meaningful behavioral detail beyond annotations: it promises exactly three normalized rows, deterministic summaries, freshness, and provenance, and implies the operation is free.
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 contain all key information with no filler. The free evaluation purpose is front-loaded, the return contents are efficiently listed, and the routing instruction to query_imported_dataset is included without repetition.
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?
Given one parameter, strong annotations, and no output schema, the description covers what matters: the tool's purpose, its zero-cost nature, the sample contents, and when to graduate to the paid query tool. No critical operational detail appears missing.
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 coverage is 100% and the single parameter dataset_id is already well-described as 'Exact query-ready dataset ID returned by list_imported_datasets.' The description adds no deeper parameter semantics, but none are needed given the complete schema.
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 states a precise verb ('sample'), the resource ('query-ready official dataset'), and the purpose ('evaluate ... before paying'). It also lists concrete outputs and names the sibling it is not, clearly distinguishing it from query_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?
Explicitly frames this as the free pre-purchase evaluation step and instructs that query_imported_dataset should only be used after the sample proves useful. This gives unambiguous sequencing and an alternative, so an agent knows exactly when to choose this tool.
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.
Each tool maps to a distinct lifecycle stage: discovery, materialization, sampling, querying, aggregation, and payment. The main ambiguity is between search_public_datasets and search_discovered_datasets, plus some overlap between get_coverage_status and list_official_sources, but the descriptions provide enough guidance for most selections.
All tools use a consistent verb_object snake_case pattern with clear verbs: get_, list_, query_, request_, sample_, search_, and aggregate_. State-changing actions uniformly use request_, and status reads uniformly use get_.
With 14 tools, the server is well within the ideal range and each tool earns its place across the data lifecycle: discover, materialize, sample, query, aggregate, and manage access. The count feels complete without being padded.
The set covers discovery, materialization, sampling, querying, aggregation, coverage status, and paid access, with provenance embedded throughout. Minor gaps exist around the 'insights' promised in the server name and lifecycle operations like cancellation or removal, but agents can generally complete core workflows.