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reg_search

Read-onlyIdempotent

Search the Federal Register (the daily journal of the US government) for rules, proposed rules, notices, and presidential documents by keyword, with optional agency, document type, and publication-date filters. Returns each document's number, title, type, publishing agency, abstract, and URL. Use reg_document to get the full text of one document.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
termNoKeyword(s), e.g. 'methane emissions', 'overtime rule'.
typeNoOptional document type: 'rule', 'proposed-rule', 'notice', or 'presidential-document'.
limitNoMax rows (default 20, max 100).
agencyNoOptional agency slug, e.g. 'environmental-protection-agency', 'securities-and-exchange-commission'.
published_toNoPublished on/before, ISO date yyyy-mm-dd.
published_fromNoPublished on/after, ISO date yyyy-mm-dd.

TDQS

A4.3/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

The read-only annotations already cover safety, and the description adds useful behavioral context: it returns a defined set of fields per document and implicitly signals that full text is not included by pointing to reg_document. It does not discuss pagination or result sorting, but this is not critical given the schema's limit parameter.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Two sentences deliver the purpose, scope, available filters, return fields, and the sibling tool for full text. There is no redundancy or filler, and the most important information is front-loaded.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a well-documented 6-parameter search tool with read-only annotations and no output schema, the description provides sufficient context: what is searched, what filters exist, what each result contains, and where to go for full text. Nothing essential is missing.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the schema fully documents all six parameters. The description adds only a high-level summary of keyword, agency, document type, and date filters, which is helpful but does not go beyond the schema's existing documentation.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description names a specific verb and resource: 'Search the Federal Register' for documents by keyword with optional filters. It lists the exact document types and clearly differentiates itself from the sibling reg_document by noting that tool is for full text retrieval.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

It explicitly tells the agent to use reg_document when full text is needed, which is key routing guidance. It does not mention the related reg_cfr_search sibling or state exclusions, but the Federal Register context makes the intended use reasonably clear.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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TDQS

B3.2/5.0
Disambiguation2/5

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.

Naming Consistency3/5

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.

Tool Count1/5

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.

Completeness4/5

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.

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