request_capabilityUse this state-changing tool only when existing discovery and coverage tools cannot satisfy the task. Records demand for a missing dataset, aggregation, insight, filter, freshness level, or export; repeated requests increase autonomous build priority.
| Name | Required | Description | Default |
|---|---|---|---|
| dataset | Yes | ||
| capability | Yes | ||
| source_url | No | ||
| example_query | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already establish the non-read-only, non-idempotent, non-destructive profile, and the description agrees with them while adding a useful behavioral detail: repeated requests 'increase autonomous build priority.' This gives the agent a clearer model of consequences, though it does not describe response behavior or other side effects beyond recording demand.
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 deliver the state-changing nature, the usage restriction, the full scope of what can be requested, and the build-priority consequence with no filler. The key gating information is front-loaded.
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?
The definition covers what the tool does, when to use it, and the behavioral consequence of repeated calls, and the annotations cover the safety profile. It would be more complete with explicit sibling routing and optional-parameter semantics, but for a simple request-recording tool without an output schema, nothing critical is 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?
With 0% schema description coverage, the description partially compensates by clarifying that `capability` refers to a missing dataset, aggregation, insight, filter, freshness level, or export. However, it does not explain `source_url` or `example_query` at all, relying on their names and optional status for inference.
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 ('Records demand') and a clear resource ('missing dataset, aggregation, insight, filter, freshness level, or export'), so the tool's function is unambiguous. It also distinguishes itself from read-oriented tools by labeling itself state-changing, though it does not explicitly differentiate from sibling request tools like request_dataset_materialization or request_paid_access.
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 an explicit when-to-use condition: 'only when existing discovery and coverage tools cannot satisfy the task.' This clearly gates the tool's use, but it does not name specific alternative tools or exclusions, leaving some routing decisions to the agent.
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.