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Glama

Ecfr

by io.github.pipeworx-io

Title Structure

title_structure
Read-onlyIdempotent

Get the top-level structure (chapters/subtitles) of one CFR title — the agencies and major divisions within that title. Returns a summarized one-level view, not the full deep tree. Keyless.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
dateNoOptional point-in-time date, YYYY-MM-DD. If omitted, the title's current 'up_to_date_as_of' date is used automatically.
titleYesCFR title number, 1–50 (e.g. 14 = Aeronautics and Space).

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: +[
+  {
+    "title": 14
+  },
+  {
+    "date": "2024-01-15",
+    "title": 29
+  }
+]
  2. First observed

TDQS

A3.7/5.0
Behavior4/5

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

Annotations already indicate read-only, idempotent, non-destructive. Description adds behavioral context about the output being a summarized one-level view (not full deep tree) and notes 'Keyless'. No contradiction with annotations.

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 very short (two sentences) and front-loaded. However, 'Keyless' is cryptic and could be expanded for clarity. No wasted words, but minor reduction in clarity for this term.

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 simplicity (2 parameters, clear schema and annotations), the description adequately explains the output nature and limitations. Without an output schema, it provides enough context about what is returned (agencies, major divisions, one-level view).

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 coverage is 100%, so schema fully documents parameters. Description does not add additional semantic information about parameters beyond what schema provides. The term 'Keyless' does not relate to parameters.

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?

Description clearly states the tool retrieves the top-level structure of one CFR title, listing agencies and major divisions, and distinguishes it from a full deep tree. Verb and resource are specific, and it differentiates from siblings like get_section_text and list_titles.

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

Usage Guidelines2/5

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

No explicit guidance on when to use this tool versus alternatives. While it mentions returning a 'one-level view', it does not indicate when to prefer this over other tools or what cases to avoid.

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