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Glama

Federal Register

by io.github.pipeworx-io

Search Documents

search_documents
Read-onlyIdempotent

Search the US Federal Register by topic / keyword for proposed rules, final rules, notices, and presidential documents. Use this whenever the question mentions a SUBJECT ("EV tax credits", "AI export controls", "PFAS regulations", "clean energy", "ozempic labeling", etc.) — recent_rules takes no topic filter and would return random unrelated rules. Returns title, abstract, agency, publication date, links. Examples: search_documents({query: "EV tax credit", type: "rule"}), search_documents({query: "artificial intelligence", agency: "commerce-department"}), search_documents({query: "Strait of Hormuz", since: "365d"}). Pass since to constrain to recent documents — without it the relevance ranker can return decade-old docs for sparse-term queries.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
typeNoDocument type filter: "rule", "proposed_rule", "notice", "presidential_document"
queryYesSearch keywords (e.g., "clean energy tax credit")
sinceNoPublication date floor. Accepts ISO date ("2025-01-01") or shorthand ("30d", "12m", "365d", "1y"). Recommended for any topical search to avoid stale results — the relevance ranker can surface 2004-2008 documents for queries with sparse hits.
agencyNoAgency slug filter (e.g., "environmental-protection-agency", "securities-and-exchange-commission")

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYesThe search query string
totalYesTotal count of matching documents
returnedYesNumber of documents returned in this response
documentsYesArray of formatted Federal Register documents

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

TDQS

A4.6/5.0
Behavior4/5

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

Annotations already indicate read-only, idempotent, non-destructive behavior. Description adds details about return format and the relevance ranker's tendency to surface old documents for sparse queries. No contradictions.

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?

Description is somewhat long but well-structured: purpose first, then usage context, examples, and important note about 'since'. Every sentence adds value; no redundancy.

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?

Given 4 parameters with full schema coverage, an output schema, and a comprehensive description covering purpose, usage, parameters, and behavior, the description is complete and requires no additional information.

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

Parameters4/5

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

Schema coverage is 100%, providing baseline 3. Description adds value by explaining the 'since' parameter's purpose and recommending its use, describing document types, and providing examples beyond schema.

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 tool searches the US Federal Register by topic/keyword for specific document types, and explicitly contrasts with sibling tool 'recent_rules' which lacks topic filtering. It also lists return fields (title, abstract, agency, publication date, links).

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

Usage Guidelines5/5

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

Explicitly advises use when question mentions a subject, contrasts with 'recent_rules' for unfiltered results, and provides examples and a warning about using 'since' to avoid stale results from the relevance ranker.

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

A3.5/5.0
Disambiguation1/5

Several tools are nearly indistinguishable: ask_pipeworx and ask_pipeworx_beta are explicitly identical in behavior, and ask_pipeworx_grounded overlaps heavily with them. Additionally, entity_profile, compare_entities, deep_research, and validate_claim all cover similar company/factual research territory, creating frequent selection ambiguity.

Naming Consistency2/5

Naming mixes multiple conventions: descriptive lowercase phrases (ai_visibility_check, compare_entities, valid claim) coexist with verb_noun (search_documents, recent_rules) and inconsistent underscores (ask_pipeworx vs ask_pipeworx_grounded, resolve_entity). There is no single recognizable pattern.

Tool Count1/5

The server is named 'Federal Register' but only 3 of 34 tools (search_documents, recent_rules, get_document) relate to that domain. The other 31 tools form a sprawling Pipeworx data and prediction-market suite, making the count extreme and inappropriate for the declared purpose.

Completeness2/5

For the stated Federal Register domain, the surface is minimal: search, recent listing, and single-document retrieval, with no docket browsing, full-text search within documents, or agency-specific navigation. The broader Pipeworx capability set is comprehensive but irrelevant to the server's name, leaving obvious gaps for the actual purpose.

Resources