request_dataset_materializationQueue a discovered official dataset for prioritized detached ingestion. This free idempotent operation returns a durable job ID; poll get_materialization_status until it supplies sample and query URLs.
| Name | Required | Description | Default |
|---|---|---|---|
| discovered_dataset_id | Yes | Exact catalog ID returned by search_discovered_datasets. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already cover idempotency and non-destructiveness, so the description adds value by disclosing that the operation is free, asynchronous/detached, returns a durable job ID, and requires polling for completion. This goes meaningfully beyond the structured annotations, though details like failure modes or expected latency are omitted.
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?
A single sentence front-loads the action and resource, then packs the key behavioral facts—free, idempotent, durable job ID, polling target—without any wasted words. Every clause earns its place.
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?
With one required parameter, no output schema, and no nested objects, the description still covers what the caller gets (a durable job ID) and what to do next (poll get_materialization_status for sample and query URLs). This is sufficient for an agent to invoke the tool correctly and know the expected follow-up.
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% because discovered_dataset_id is documented as the exact catalog ID returned by search_discovered_datasets. The description's 'discovered official dataset' phrasing adds only minor semantic color and does not provide additional parameter-level detail, so the baseline score of 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 uses a specific verb ('Queue') and resource ('discovered official dataset') and clearly states the goal: making the dataset query-ready. It also differentiates from siblings by naming the follow-up tool, get_materialization_status, and framing this as the ingestion trigger rather than a status or query operation.
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 gives a clear usage context: call this to queue a discovered dataset for ingestion, then poll get_materialization_status. It does not explicitly state when not to use it or list alternatives, but the workflow is unambiguous enough for an agent to select it appropriately among the siblings.
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.