Check whether output from an AI tool's free tier may be used commercially, based on the vendor's official terms (verdicts: yes / no / conditional / depends on the model used / not stated). Not legal advice; details and obligations live on the publish-check page.
| Name | Required | Description | Default |
|---|---|---|---|
| lang | No | Data language, default en | |
| tool | Yes | Tool slug or name |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description discloses expected output (verdicts: yes/no/conditional/depends on model/not stated) and qualifies itself as not legal advice with details on the publish-check page. This is meaningful behavioral context beyond the schema.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences with front-loaded purpose. The first sentence states exactly what the tool checks; the second adds a caveat that is short and relevant. No unnecessary words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity (two parameters, no output schema), the description fully covers what the tool does and what to expect. It even points to external details for obligations. Sibling context further clarifies scope.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%: both 'lang' and 'tool' are documented in the schema. The description adds no parameter-specific detail, so the baseline of 3 applies.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb and object ('Check whether output... may be used commercially') and specifies the basis (vendor's official terms). It clearly distinguishes itself from sibling tools like get_free_tier_limit and search_ai_tools, which address different concerns.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies when to use this tool: whenever a commercial-use determination for AI free-tier output is needed. It does not explicitly name alternatives or exclusions, but the clear scope and sibling context provide sufficient guidance.
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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Each tool targets a clearly distinct task: querying limits, checking commercial use, fact-checking claims, comparing categories, searching the directory, auditing stacks, building workflows, and monitoring changes. Even the two change-related tools are differentiated by one being a query and the other a subscription.
All tool names follow a consistent lowercase snake_case verb_noun pattern (audit_, build_, check_, compare_, explain_, find_, get_, search_, watch_). Repeated verbs like check_ and get_ are paired with distinct objects, making the pattern predictable and easy to scan.
At 14 tools, the set is well-scoped for the breadth of the domain (verifying free tiers, checking commercial use, tracking changes, building workflows, and China-specific rules). Each tool earns its place without redundancy, fitting comfortably within the ideal range.
The surface covers the full lifecycle: querying a single tool's limit, comparing across categories, searching the directory, fact-checking claims, explaining missing data, finding alternatives, auditing a stack, calculating quota fit, and both reading and subscribing to changes. No obvious dead ends or missing operations for the stated purpose.