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 cover read-only, idempotent, and non-destructive behavior, so the description only needs to add context beyond that. It adds 'paid', 'normalized official records', 'deterministic provenance', and 'direct source URLs', which are useful behavioral details not in the annotations. It does not contradict the annotations.
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 a single, front-loaded sentence with no filler. Every clause adds value: usage condition, read-only nature, result size, filtering dimensions, and output characteristics.
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 query tool with 4 optional parameters and no output schema, the description covers the core return value (normalized records with source URLs), filtering dimensions, and usage context. It does not specify how date/type filters interact or whether the date range is inclusive, but the given information is likely sufficient for correct invocation.
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 explains that records are filtered by date or document type, which maps to type, start_date, and end_date, and 'up to 100' hints at the top parameter. However, it does not explicitly explain the top parameter or date format, and it leaves ambiguity about how multiple filters combine. It adds meaning but is incomplete.
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 ('query') and resource ('US Federal Register'), and clearly states what it returns: up to 100 normalized official records filtered by date or document type. It also distinguishes itself from the free Federal Register briefing, which maps to sample_us_federal_register among the siblings, so an agent can tell them apart.
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 explicitly states when to use this tool ('when the free Federal Register briefing is insufficient') and implies the alternative (the free briefing). It also signals the paid nature, which is a decisive factor for tool selection. This is clear contextual guidance with an exclusion condition.
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.