Tollmint

legal_ai-disclosure

Binding obligations worldwide to disclose, label, or watermark AI-generated content — EU AI Act Art. 50, California SB 942 and AB 2013, China’s 2025 labelling Measures, South Korea’s AI Basic Act, India’s 2026 IT Rules. Each row gives the exact trigger condition, what must be done, the exemptions, the penalty ceiling, and whether anyone has been enforced against yet. Includes forward dates for obligations already enacted but not yet biting, and records the California election-deepfake laws that were struck down in 2025 and are still widely cited as live. Query before shipping generated content into a jurisdiction. 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`dead_only`	No	Return only rules that are NOT live law — vacated, superseded, or still proposed.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

A4.4/5.0

Behavior5/5

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

No annotations are provided, so the description carries full burden. It discloses forward dates, dead rules (e.g., struck-down California laws), and notes that 'verified_only' may be blocked due to source restrictions. This is thorough and honest.

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 front-loaded with the core purpose and concise, though it contains three sentences. It efficiently conveys essential information without redundancy.

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?

Given the absence of an output schema, the description explains what each row contains (trigger, action, exemptions, penalty, enforcement). It covers all necessary context for a legal compliance tool, including edge cases like forward dates and dead laws.

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 83% (5 of 6 parameters have descriptions). The description does not add parameter-specific guidance beyond the schema, and the 'limit' parameter lacks a description. Baseline 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 the tool provides binding obligations for disclosure, labeling, or watermarking of AI-generated content, citing specific laws and jurisdictions. It distinguishes itself from sibling tools by focusing on legal compliance rather than detection or design.

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 explicitly advises to 'Query before shipping generated content into a jurisdiction,' providing clear usage context. While it doesn't mention when to avoid using the tool, the purpose is sufficiently scoped, and the free nature is noted.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All Tollmint tools Inspect

Try in Browser

RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

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

Need Help?