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?
Beyond annotations declaring readOnly, openWorld, idempotent, and non-destructive, the description discloses the exact response profile: three records, deterministic aggregates over the latest 25 documents, direct URLs, provenance, and rate-limit status. This is substantial behavior context that the annotations alone do not convey.
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 are tightly front-loaded with the key purpose and use cases, followed by a compact list of return contents. Every phrase contributes information; there is no filler.
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 no output schema, the description fully covers what an agent needs to know: the target data, the evaluation context, the exact number of records, the aggregation window, and the included metadata. No critical context appears to be 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?
The tool has zero parameters and an empty input schema, so there is no parameter semantics burden. The description reinforces that the tool is free and requires no auth, which is sufficient for a no-parameter 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 names a specific verb and resource: 'Use this free no-auth tool for current US regulatory activity' and explains what it returns: three Federal Register records, aggregates, URLs, provenance, and rate-limit status. It also positions the tool as a sample/evaluation entry point, distinguishing it from a paid or more complete offering.
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 clearly states two contexts: monitoring current US regulatory activity and evaluating AgentNative before paying. It does not explicitly name sibling alternatives such as query_us_federal_register or state when not to use this sample, so it stops short of full exclusion guidance.
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.
Each tool maps to a distinct lifecycle stage: discovery, materialization, sampling, querying, aggregation, and payment. The main ambiguity is between search_public_datasets and search_discovered_datasets, plus some overlap between get_coverage_status and list_official_sources, but the descriptions provide enough guidance for most selections.
All tools use a consistent verb_object snake_case pattern with clear verbs: get_, list_, query_, request_, sample_, search_, and aggregate_. State-changing actions uniformly use request_, and status reads uniformly use get_.
With 14 tools, the server is well within the ideal range and each tool earns its place across the data lifecycle: discover, materialize, sample, query, aggregate, and manage access. The count feels complete without being padded.
The set covers discovery, materialization, sampling, querying, aggregation, coverage status, and paid access, with provenance embedded throughout. Minor gaps exist around the 'insights' promised in the server name and lifecycle operations like cancellation or removal, but agents can generally complete core workflows.