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regulatory_search_open_rulemakings

Search for open rulemakings and comment periods from Regulations.gov and Federal Register using keywords and agency filters. Returns key details like title, deadline, and document count.

Instructions

Search open rulemakings and comment periods on Regulations.gov and Federal Register. Returns docket title, agency, comment deadline, docket ID, and document count in AI-Ready Markdown. Verified source: Regulations.gov + Federal Register API. Token-efficient. Data freshness: 4-hour cache. status: 'open', 'closed', 'all'. Default: 'open'. Example: search_open_rulemakings('artificial intelligence', 'FTC', 'open')

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
keywordYes
agencyNo
statusNoopen

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior3/5

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

With no annotations, the description bears full burden. It discloses data freshness (4-hour cache), source verification, and token-efficiency. However, it does not mention whether the tool is read-only, any rate limits, or authentication requirements, which would be helpful for an agent.

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

Conciseness4/5

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

The description is concise (6 sentences) and front-loaded with the main purpose. It includes essential details like sources, return format, and example without unnecessary verbosity. Slightly long but still efficient.

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

Completeness4/5

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

Given the tool has 3 parameters, no annotations, and exists among many siblings, the description covers purpose, sources, output format, data freshness, and an example. It does not mention pagination or limits, but for a search tool with an output schema, this is adequate.

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 0%, so the description must compensate. It explains the 'status' parameter values ('open', 'closed', 'all') and the default. It provides an example that maps all three parameters. However, it does not explicitly describe what 'agency' expects (e.g., full name or abbreviation) or the expected format for 'keyword'.

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 clearly states the verb 'search' and the resource 'open rulemakings' with specific sources (Regulations.gov and Federal Register). It lists the return fields (docket title, agency, comment deadline, etc.), which distinguishes it from sibling tools like regulatory_fetch_docket_details that fetch specific dockets rather than search.

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

Usage Guidelines3/5

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

The description provides an example call and notes the default status, but does not explicitly explain when to use this tool versus other regulatory siblings (e.g., when to search vs. fetch a known docket). The usage context is implied but not directly compared to alternatives.

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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