Tollmint

verify_compliance-scan

Check marketing copy, or a live page, against 225 curated regulatory rules covering AI-content disclosure, advertising claim substantiation, subscriptions and price display, unsolicited messaging, accessibility, and automated data collection. Every finding cites the instrument and article plus a primary-source URL, and reports whether that URL resolved when the corpus was built. Crucially it also returns what is NOT law: rules commonly believed to be in force that were vacated, enjoined, or superseded — the FTC Click-to-Cancel rule, the California election-deepfake statutes, the DOT ancillary-fee rule. A scanner built by prompting a model asserts all three as live and sends you to rewrite compliant copy. Dead rules never enter the analysis and are reported separately with the reason they died. Reports rule conflicts and citations; does not determine liability. Costs $0.035000 per call.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`url`	No	Page to analyse. Fetched respecting robots.txt.
`html`	No	Raw HTML, if you already have it. Takes precedence over url.
`text`	No	Marketing copy to check. Supply this, or a url, or html.
`areas`	No	Limit the scan to these rule sets. Omit to check every one.
`jurisdiction`	No	Restrict to rules for this jurisdiction. "any" checks all of them.	any

TDQS

A4.4/5.0

Behavior5/5

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

No annotations provided, so description carries full burden. It discloses fetching with robots.txt, citing sources with URLs, reporting dead rules, and not determining liability, giving clear behavioral expectations.

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 thorough but slightly verbose. Each sentence adds value, but some detail (e.g., specific dead rules) could be more concisely stated without losing clarity.

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?

Complex tool with 5 parameters and many siblings, but no output schema. Description covers input options, return info (citations, dead rules), and limitations (no liability determination), making it complete for agent usage.

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 covers all 5 parameters with descriptions, so baseline is 3. The description adds overall context (e.g., cost, rule count) but does not enhance parameter-level semantics 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 checks marketing copy or live pages against 225 curated regulatory rules, covering specific areas like AI-disclosure, claims, subscriptions, etc. It distinguishes from more specific sibling tools (e.g., legal_claims) by being a comprehensive scan.

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

Usage Guidelines4/5

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

The description explains inputs (url, html, or text) and notes the tool does not determine liability. It implicitly provides context for when to use (compliance checks) but does not explicitly state when not to use or suggest alternative sibling tools.

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

A4/5.0

Disambiguation5/5

All 20 tools have clearly distinct purposes, grouped by domain prefixes (content_ai, design_ai, legal, psych, ref, tollmint, web). Even similar-sounding tools like psych_dark-pattern-detect and psych_dark-patterns are differentiated as a live scanner versus a reference taxonomy. No two tools overlap in functionality.

Naming Consistency4/5

Naming mostly follows a prefix_descriptive pattern, but some tools use hyphens (content_ai-score, design_ai-slop-detect) while others use underscores (legal_accessibility, psych_biases). This minor inconsistency prevents a perfect score, but the pattern is still clear and readable.

Tool Count4/5

20 tools is slightly above the typical 'well-scoped' range, but each tool serves a specific, justifiable need across multiple domains (legal, psychology, content analysis, geocoding, internal). The count feels comprehensive rather than bloated.

Completeness5/5

The tool surface covers all major areas implied by the domain prefixes: comprehensive legal compliance references, extensive psychology/behavioral design tools, content and design analysis, geocoding, and internal server management. No obvious gaps for the intended use cases.

Resources

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