query_us_federal_registerUse this paid read-only tool when the free Federal Register briefing is insufficient. Returns up to 100 normalized official records filtered by date or document type, with deterministic provenance and direct source URLs.
| Name | Required | Description | Default |
|---|---|---|---|
| top | No | ||
| type | No | ||
| end_date | No | ||
| start_date | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnly, openWorld, idempotent, and non-destructive hints. The description adds valuable behavioral context beyond annotations: paid nature, result cap of 100, normalization, filtering by date/document type, deterministic provenance, and direct source URLs. No contradiction exists.
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 tightly constructed sentences that front-load the key usage condition and then state the output characteristics. Every phrase adds useful information, with no filler or redundancy.
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 is largely complete for a read-only query tool: it covers the trigger condition, result count, filter dimensions, normalization, provenance, and URLs. However, it does not clarify whether filters are combinable or how start_date and end_date relate, and with no output schema, a bit more specificity about the record shape would improve completeness.
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 for the four parameters. It mentions 'up to 100' (top), 'date' (start_date/end_date), and 'document type' (type), but it does not explain each parameter's role, relationships, or defaults. The top limit and date/type filters are only vaguely mapped, leaving significant semantics to be inferred.
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 this tool queries the US Federal Register and returns up to 100 normalized official records, with a specific verb, resource, and output scope. It also differentiates itself from the free Federal Register briefing, effectively pointing to its sibling sample_us_federal_register.
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?
'Use this paid read-only tool when the free Federal Register briefing is insufficient' explicitly provides a condition for selection and implies the alternative for the opposite case. This is direct guidance for when to choose this tool over the free briefing option.
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.