Tollmint

legal_scraping

What courts have actually held about scraping, crawling, and training-data acquisition. Answers questions in the form an engineer asks them: does ignoring robots.txt create liability, does a browsewrap bind a bot, is public data outside the CFAA. Records binding scope honestly — a Ninth Circuit holding is not US law — and tracks the full history of hiQ v LinkedIn, which is routinely cited as making scraping legal and which hiQ ultimately lost on contract. Covers the EU text-and-data-mining exceptions and their opt-out. 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`dead_only`	No	Return only rules that are NOT live law — vacated, superseded, or still proposed.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

A4.1/5.0

Behavior4/5

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

Since no annotations are provided, the description carries full burden for behavioral disclosure. It honestly notes limitations (Ninth Circuit is not US law) and states it's free with no account required. This adds transparency, though it omits mention of potential rate limits or data freshness.

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 detailed but efficiently packed with essential info. It front-loads the main purpose and uses examples to clarify scope. A minor reduction for length could improve conciseness, but it remains structured and helpful.

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?

For a tool with no output schema and moderate complexity, the description covers scope, limitations, cost, and account requirements. It could mention result format or pagination, but overall it provides sufficient context for an agent to use the tool correctly.

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%, so most parameters already have descriptions. The tool description does not add new meaning to parameters beyond what the schema provides. Baseline 3 is appropriate given high coverage.

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 court holdings on scraping, crawling, and training-data acquisition. It gives concrete examples of questions it answers and specifies the perspective ('as an engineer asks'). This distinguishes it from sibling tools like legal_accessibility or legal_ai-disclosure which cover different legal topics.

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 provides context on when to use the tool (e.g., for scraping legality, EU exceptions, hiQ case history) but does not explicitly state when not to use it or offer alternative tools. However, the examples and scope make usage relatively clear.

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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