sample_us_federal_registerUse this free no-auth tool for current US regulatory activity or to evaluate AgentNative before paying. Returns three Federal Register records plus deterministic aggregates over the latest 25 documents, direct record URLs, provenance, and rate-limit status.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare read-only, open-world, idempotent, and non-destructive behavior, and the description adds meaningful context beyond that: no authentication required, free, returns exactly three records plus aggregates over the latest 25 documents, direct URLs, provenance, and rate-limit status. It also signals deterministic output, which is useful behavioral information.
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 sentences with no filler: the first sentence gives use cases and the free/no-auth positioning, the second enumerates the return contents. Every clause adds value, and the most important scoping 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?
For a zero-parameter sampling tool, the description fully covers what an agent needs: purpose, access constraints, and the exact nature of the returned data. The absence of an output schema is mitigated by the explicit list of return components, including record URLs, provenance, and rate-limit status.
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 has zero parameters and the schema description coverage is 100%, so there is no parameter documentation burden. The description appropriately focuses on behavior and return payload rather than parameters, which is the correct approach for a no-input tool.
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 states a specific verb ('Get a free Federal Register briefing'), resource ('US regulatory activity'), and outcome ('three Federal Register records plus deterministic aggregates'). It distinguishes itself from query-style siblings by emphasizing the sample nature and free no-auth access. The title and description align without tautology.
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 provides clear when-to-use context: for current US regulatory activity or to evaluate AgentNative before paying. It implies this is a limited free sample versus paid or fuller alternatives, but it does not explicitly name a sibling such as query_us_federal_register for exhaustive queries, so a small gap remains.
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 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.