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 provide readOnlyHint, openWorldHint, idempotentHint, and destructiveHint, so the description's main job is to add context beyond those. It adds the paid requirement, the 100-record cap, the normalization of records, deterministic provenance, and direct source URLs—all useful behavioral details not present in 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?
One dense sentence carries the trigger condition, cost context, result cap, filter dimensions, provenance guarantees, and output format. Every clause earns its place and the most important guidance—when to use—comes first.
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?
There is no output schema, so the description should describe return shape, and it does: up to 100 normalized official records with direct source URLs and deterministic provenance. It does not list specific returned fields or clarify behavior when no filters are provided, but for a read-only query tool with this parameter set, the guidance is reasonably complete.
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 silent parameters. It maps 'up to 100' to the top parameter, 'document type' to the type enum, and 'date' to start_date/end_date. It does not explain whether dates are inclusive or how top behaves when omitted, but the schema itself provides formats and bounds.
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 states a specific verb ('query'), a resource ('US Federal Register'), and the scope ('returns up to 100 normalized official records filtered by date or document type'). It clearly differentiates itself from the free Federal Register briefing and sibling tools like 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?
It explicitly says to use this paid tool when the free Federal Register briefing is insufficient, giving a clear trigger condition and implying the free alternative should be used otherwise. However, it does not name sibling tools explicitly or explain when to prefer this over query_imported_dataset or list_official_sources.
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.