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Glama

Ecfr

by io.github.pipeworx-io

Search Regulations

search_regulations
Read-onlyIdempotent

Full-text search across all 50 titles of the US Code of Federal Regulations (federal agency regulations) via the official eCFR API. Returns matching sections with citation, heading, and excerpt. e.g. "drone operation", "food labeling", "overtime pay". Optionally restrict to one CFR title number. Keyless.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
pageNoPage number for pagination (default 1).
limitNoMax results to return (default 10, max 20).
queryYesSearch phrase, e.g. "drone operation", "food labeling", "overtime pay", "hazardous waste".
titleNoOptional CFR title number (1–50) to restrict the search to. Use list_titles to see the title index, e.g. 14 = Aeronautics and Space, 21 = Food and Drugs, 29 = Labor.

Schema Changelog

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

  1. Changed1 schema field changed
    • addedInput schema / examples
      Added value: +[
+  {
+    "query": "drone operation"
+  },
+  {
+    "limit": 15,
+    "query": "food labeling",
+    "title": 21
+  }
+]
  2. First observed

TDQS

A3.7/5.0
Behavior3/5

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint=false, so the safety profile is clear. The description adds minor behavioral context: the tool uses the 'official eCFR API' and is 'Keyless'. This provides some additional transparency but does not significantly expand beyond what annotations convey.

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 relatively concise at about 4 sentences, with the key action front-loaded. It includes necessary details like the output (citation, heading, excerpt) and optional restrictions. While it could be slightly tighter, there is no wasted wording.

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's complexity (search across 50 titles), the description covers the main functionality, output format, and optional title restriction. Pagination is handled by schema-described parameters. No output schema exists, but the return structure is briefly mentioned. Some caveats or result size limits could be added, but overall it is adequately complete.

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%, with all 4 parameters described in detail. The description provides examples like 'drone operation' but these are already present in the schema's 'examples' field. The description does not add new semantic meaning beyond what the schema already offers, so a baseline score of 3 is appropriate.

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 it performs full-text search across all 50 titles of the US Code of Federal Regulations, specifically naming the resource and action. Examples like 'drone operation' reinforce the purpose. While it doesn't explicitly distinguish from sibling tools, the verb-resource combination is specific enough.

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 implies usage scenarios through examples and mentions optional restriction by title number. However, it does not provide explicit guidance on when to use this tool versus alternatives like 'list_titles' or 'title_structure', nor does it state when not to use it.

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

A3.7/5.0
Disambiguation2/5

Multiple tools have heavily overlapping purposes: ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded are near-duplicates (beta is currently identical), and ai_visibility_check vs scan_competitor_ai_presence plus deep_research vs ask_pipeworx create real selection ambiguity. Some clusters like the memory trio and CFR read tools are distinct, but the overall set is confusing.

Naming Consistency3/5

Most tools use snake_case and many follow a verb_noun pattern (search_regulations, generate_llms_txt, validate_claim), but noun-first names (entity_profile, title_structure, ai_visibility_check) and prefix families (polymarket_*, pipeworx_*) break the pattern. The conventions are mixed but still readable and mostly predictable.

Tool Count2/5

35 tools is heavy for any single server, and the bulk of them (Polymarket betting, memory, AI visibility, npm scanning, subscriptions) are unrelated to the server's 'Ecfr' name, which suggests a narrow regulatory focus. This is a kitchen-sink scope, making the count feel bloated rather than well-scoped.

Completeness3/5

The eCFR-specific surface is thin — list_titles, search_regulations, get_section_text, and title_structure cover basic read/search but lack version history, update tracking, or agency-level navigation. Other mini-domains (data lookup, polymarket, subscriptions, memory) are individually fairly complete, but the absence of a unified purpose leaves clear gaps overall.

Resources