Tollmint

legal_claims

Which marketing claims legally require evidence, and what evidence suffices. Covers superlatives, health and efficacy, "free", environmental and carbon-neutral claims, reference and "was/now" pricing, "up to X%", reviews and testimonials, country of origin, guarantees, and AI-capability claims. Each row gives the regulator’s own evidentiary standard, the common way advertisers fail it, and enforcement with penalties where it exists. Also records five widely repeated "you can’t say that" rules that no instrument actually supports. Free — this call costs nothing. No account or payment required.

Input Schema

TableJSON Schema

Name	Required	Description
`limit`	No
`search`	No	Free-text over summary, instrument and detail.
`status`	No	Filter to one lifecycle state.
`dead_only`	No	Return only rules that are NOT live law — vacated, superseded, or still proposed. These are the rules most often wrongly believed to be in force.
`jurisdiction`	No	Substring match, e.g. "EU", "UK", "US-federal", "California", "Germany".
`verified_only`	No	Only rows whose source URL resolved when last checked. Off by default: many primary sources (courts, national gazettes) block automated checkers, so "blocked" is not "bad".

TDQS

A4/5.0

Behavior4/5

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

No annotations provided, so description carries full burden. It discloses that the tool is free, requires no account, and describes output content. However, it does not explicitly state read-only behavior or rate limits, which are common for info retrieval tools. Still, transparency is good for a non-destructive data query tool.

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?

Description is concise and well-structured: first paragraph lists covered topics and row structure, second addresses debunked rules and cost. Every sentence adds value, no redundancy. Front-loaded with purpose.

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 no output schema, description adequately explains return structure (each row includes evidentiary standard, failure mode, enforcement). Also mentions additional debunked rules. Parameters are covered by schema. Complete enough for an agent to understand what it will receive, though lacking field names.

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 83% and parameters have descriptive names/schema descriptions. The tool description adds marginal value beyond schema by providing context on data coverage (e.g., claims types). It does not directly explain parameter semantics, but schema already does most of the work.

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's purpose: it provides information on which marketing claims legally require evidence and what evidence suffices, listing covered topics (superlatives, health, etc.) and row structure. This distinguishes it from siblings like legal_messaging and legal_ai-disclosure.

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?

No explicit guidance on when to use vs. alternatives. The description mentions that it's free and requires no account, but does not state when to prefer this tool over siblings like legal_messaging. Usage is implied from purpose, but lack of explicit exclusions or alternatives is a gap.

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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