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?
The description states the tool is state-changing and discloses the key behavioral consequence: repeated requests increase autonomous build priority. This goes beyond the annotations, which only flag readOnlyHint=false and idempotentHint=false, by explaining how requests accumulate. It does not cover authentication, persistence, or response behavior, but enough is added to be useful.
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?
The description is two tight sentences with no filler. It front-loads the critical usage gate ('only when existing discovery and coverage tools cannot satisfy the task'), then immediately enumerates the supported capability types and the demand-accumulation behavior.
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 description gives enough context for an agent to decide when to call the tool, what it does, and what repeated calls mean. It omits explicit guidance on optional parameters and sibling disambiguation, but for a request-recording tool with a simple schema, this is a solid and mostly complete definition.
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 description coverage is 0%, so the description must compensate. It does cover the required parameters conceptually by listing dataset and capability types, but it gives no explanation of the optional source_url or example_query parameters. Those remain implicit and left to inference from their names.
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 clearly identifies the tool as a state-changing request that records demand for missing capabilities, and enumerates concrete capability types (dataset, aggregation, insight, filter, freshness, export). It distinguishes itself from discovery and coverage tools, but does not explicitly differentiate itself 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 usage condition: use only when existing discovery and coverage tools cannot satisfy the task. This provides a clear threshold, but it does not name specific alternative tools or state when request_capability should be avoided in favor of a specific 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.