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?
The description adds valuable behavioral context beyond the annotations: the operation is free, asynchronous, durable (returns a job ID), and requires polling until sample/query URLs are available. It discloses the detached ingestion nature and the need to check status, which significantly exceeds what annotations alone provide. No contradiction with 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 sentences with zero fluff. The core action is front-loaded, the return value is stated, and the next-step polling instruction is included. Everything 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?
For a single-parameter async queueing tool with no output schema, the description is complete: it names the required identifier, the operation's effect, the return value, and the polling endpoint. An agent has enough information to invoke the tool correctly and know what to do next.
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?
The input schema provides 100% coverage with a clear description of discovered_dataset_id as an exact catalog ID returned by search_discovered_datasets. The tool description does not add extra parameter-level detail, but given full schema coverage, 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 states a specific verb ('Queue') and resource ('discovered official dataset'), plus the operational effect: prioritized detached ingestion. It also names the follow-up tool (get_materialization_status), which distinguishes this tool as the request/trigger step versus the polling step.
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 clearly implies the workflow: after discovering a dataset, queue it for materialization; then poll get_materialization_status for sample and query URLs. It does not explicitly state when not to use this tool versus alternatives, but the polling instruction provides solid context and routes the agent to the correct sibling.
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.