get_materialization_statusPoll a durable materialization job. A complete result includes the query-ready dataset ID plus free sample and paid query URLs.
| Name | Required | Description | Default |
|---|---|---|---|
| materialization_id | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, idempotentHint, and destructiveHint=false, covering safety. The description adds valuable behavioral detail about what a complete result includes (dataset ID and URLs), which goes beyond annotations. No contradictions exist, and the extra info helps the agent know what to expect from the response.
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 the action front-loaded ('Poll a durable materialization job'). No filler or repetition. Every clause adds value by specifying what a complete result includes. Efficient and well-structured.
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, read-only polling operation, the description is minimally adequate. It explains the success result but does not mention how to interpret incomplete results, what 'durable' means in practice, or that this should be called only after requesting materialization. Given the simplicity, a bit more context about the poll flow would make it complete.
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 0%, and the description does not explain the materialization_id parameter's meaning. The pattern in the schema indicates format but not semantics. The parameter name alone is insufficient for an agent to understand what value to supply and why. The description should compensate for the lack of schema description but does not.
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 action ('Poll a durable materialization job') and the exact resource, making it clear this is a status-checking operation. It distinguishes itself from request_dataset_materialization (which would create a job) and get_coverage_status (a different concept). The title reinforces the purpose without ambiguity.
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 implies usage for polling status, but does not explicitly state when to use it versus alternative tools, nor does it mention prerequisites (e.g., after requesting materialization). An agent must infer usage from the tool name and context rather than being told directly.
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.