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reg_cfr_search

Read-onlyIdempotent

Full-text search the current Code of Federal Regulations (eCFR, all 50 titles) for a phrase or keywords. Returns matching sections with their citation (e.g. '40 CFR 98.411'), hierarchy heading, a text snippet, effective date, and the official eCFR URL, plus the total match count. Use reg_cfr_section to read a full section's text.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMax sections to return (default 20, max 100).
queryYesPhrase or keywords to find in the CFR, e.g. 'greenhouse gas reporting'.

Schema Changelog

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

  1. First observed

TDQS

A4.5/5.0
Behavior4/5

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

Annotations already establish readOnlyHint, idempotentHint, and non-destructive behavior, so the safety profile is covered. The description adds meaningful behavioral detail beyond the annotations: it searches all 50 titles of the current CFR and enumerates the exact return fields (citation, hierarchy heading, snippet, effective date, URL, total count). This gives the agent a clear model of what the tool will do and return.

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?

The description is two sentences with no wasted words. The first sentence states the action and scope, and the second sentence enumerates return fields and points to the relevant alternative. The most decision-relevant 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 simple two-parameter search tool with strong annotations and full schema coverage, the description is complete. It tells the agent what is searched, what is returned, and when to use a different tool. Although there is no output schema, the description enumerates the return fields, so the agent knows what to expect.

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 input schema already documents both parameters (query and limit) with descriptions and defaults. The tool description adds no additional parameter-level meaning beyond restating that the query is a phrase or keywords. Baseline 3 is appropriate because the schema carries the parameter documentation fully.

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 opens with a specific verb and resource: 'Full-text search the current Code of Federal Regulations (eCFR, all 50 titles)'. It clearly defines what the tool does, including the kind of output it returns (matching sections with citations, hierarchy heading, snippet, effective date, URL, total match count), and it explicitly distinguishes itself from reg_cfr_section by directing full-section reads there.

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?

The description gives an explicit routing instruction: 'Use reg_cfr_section to read a full section's text.' This tells the agent when to prefer the sibling tool instead of reg_cfr_search. It clearly positions reg_cfr_search as the tool for finding relevant sections, not for retrieving complete regulatory text.

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

B3.3/5.0
Disambiguation2/5

Several tool clusters overlap heavily—company due-diligence and risk tools (counterparty_risk_score, company_trust_check, entity_dossier, issuer_diligence_dossier, resolve_entity, entity_resolve), carrier vetting tools, sanctions screening tools, and recall tools all have subtle boundary distinctions. While descriptions are detailed, an agent navigating 294 tools will frequently struggle to pick the right one.

Naming Consistency3/5

Most tools follow a readable snake_case domain-prefix pattern (fdic_, edgar_, sanctions_, congress_), which helps. However, verb placement is inconsistent—search_available_datasets vs cdc_dataset_query, resolve_entity vs entity_resolve—and synonyms like search, lookup, get, detail, fetch, and status are used interchangeably.

Tool Count1/5

294 tools is an extreme number for a single MCP server, far beyond what an agent can reliably hold in context or select from accurately. The presence of tool-group discovery helpers mitigates but does not solve the fundamental scale problem.

Completeness4/5

The data breadth is genuinely extensive, covering finance, health, legal, real estate, transportation, energy, cyber, education, and many other domains, often with generic query fallbacks. Still, some capabilities are shallow or incomplete—package tracking stops at a link, property tools are demo-only in places, and caselaw coverage is limited—so it is not a fully complete surface.