Get full metadata and a plain-text body excerpt for a single Federal Register document by its document number (e.g. '2026-09905'). Returns title, type, agencies, abstract, affected CFR parts, a leading excerpt of the full rule text, and the URL for the complete document.
| Name | Required | Description | Default |
|---|---|---|---|
| document_number | Yes | Federal Register document number, e.g. '2026-09905'. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already establish that the operation is read-only, idempotent, and non-destructive. The description adds useful behavioral context by stating that only a 'leading excerpt' of the rule text is returned, not the full document, and provides the URL for the complete document.
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 focused sentence that leads with the action and then lists the concrete return fields. There is no filler, and it conveys exactly what an agent needs to know.
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 tool with one required parameter, clear annotations, and no output schema, the description is complete: it states the input, the expected output fields, the excerpt limitation, and where the full document can be found. Nothing essential is 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 single parameter document_number is already fully documented in the schema, including the same example format '2026-09905'. The description reinforces the wording but does not add meaningful new semantic detail beyond the schema.
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 opens with a specific verb-resource pair: 'Get full metadata and a plain-text body excerpt for a single Federal Register document by its document number.' It clearly indicates this is a single-document fetch by ID, which distinguishes it from search-oriented siblings like reg_search and reg_cfr_search.
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 implies the right usage context: use this tool when you already have a specific Federal Register document number and need its metadata or text excerpt. It does not explicitly name alternatives or when-not-to-use conditions, but the single-document-by-ID framing is unambiguous.
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.
Many tools overlap heavily across domains: caselaw_search vs court_case_search vs court_opinion_search, caselaw_citation_lookup vs court_citation_resolver, and a cluster of company due-diligence tools (company_trust_check, counterparty_risk_score, entity_dossier, issuer_diligence_dossier, kyb_aml_evidence_case_file) that all screen a company for sanctions/risk/standing. With 290 tools, an agent will frequently face multiple equally plausible choices for the same user intent.
The vast majority of tools follow a clean domain-prefix + snake_case pattern (census_, eia_, fmcsa_, npi_, cfpb_, etc.), but there are notable exceptions: entity_resolve and resolve_entity are reversed duplicates, reg_search (Federal Register) sits next to reg_cfr_search (CFR) with confusingly similar names, and carrier_monitor_recheck deviates from the carrier_vetting_* family.
290 tools is an extreme count under any rubric, far exceeding even the 50+ threshold for the lowest score. While the group-filtering mechanism and meta-tools like list_tool_groups and search_available_datasets mitigate the practical burden, the raw surface is still massively oversized for an agent to select from accurately and efficiently.
For a read-only data-aggregation server, coverage is remarkably comprehensive across 59 domains, and generic fallbacks like cdc_dataset_query, eia_series_lookup, fred_observations, and bls_series prevent most dead ends. Minor gaps exist (a single GitHub tool, demo-only property_lookup coverage, no write/update operations anywhere), but the stated data-access purpose is well served.