get_coverage_statusUse this free operational tool to decide whether available public-data coverage is sufficient or whether to request a missing capability. Returns discovery, materialization, queryable-row, queue, failure, and freshness counts.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare read-only, idempotent, and non-destructive behavior, so the description's main job is to add what the tool returns and its operational nature, which it does by enumerating discovery, materialization, queryable-row, queue, failure, and freshness counts. It does not add limitations such as staleness or rate limits, but those are not critical for a status tool.
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 tight sentences lead with the use case, then enumerate the returned metrics. No redundant detail or unnecessary schema 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?
With no parameters and an output that is fully summarized as six named counts, the description gives an agent everything needed to call and interpret the tool. The absence of an output schema is compensated by the explicit list of returned metrics.
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 tool takes zero parameters and the schema coverage is 100%, so there is no parameter burden for the description to carry; the baseline is 4.
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 the tool is a read-only status check for public-data coverage, names the six metric categories it returns, and frames the decision it supports (sufficient coverage vs. requesting capability), clearly distinguishing it from siblings like request_capability.
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?
It explicitly says to use the tool when deciding whether existing coverage is sufficient or whether to request a missing capability, which points to a concrete decision context. It stops short of naming the exact sibling to use for the request, but the context is unmistakable given the sibling list.
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.