verified-ai-free-tiers

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.

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

With no annotations, the description takes on the burden of behavioral disclosure. It explains the output content (source, date, allowance details), the absence behavior, and the data quality policy ('publishes no unsourced figures'). This goes beyond a basic read operation, though it does not mention side effects or error conditions.

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?

The description is two sentences, front-loaded with the primary purpose, then expanding into result details and a usage caveat. Every sentence earns its place with no redundancy.

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 (2 params, no output schema), the description thoroughly covers what the tool returns, how to interpret the absence of results, and the verification policy. This is complete for a lookup tool and compensates for the lack of an output schema.

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?

The input schema already fully describes both parameters (tool slug/name and lang enum with default). The description does not add parameter-specific guidance, but with 100% schema coverage, the baseline of 3 is appropriate.

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 ('Get') and clearly identifies the resource ('verified free-tier limit of an AI tool'), listing concrete return items (allowance extent, wall behavior, source page, check date). It distinguishes itself from sibling tools like check_commercial_use and search_ai_tools by focusing on free-tier limits and verification.

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 usage context: retrieving verified free-tier allowance details for an AI tool. It also provides guidance on interpreting absence results ('Absence of a tool means no officially verifiable number exists'). It does not explicitly name alternatives, but the sibling tool names make differentiation clear.

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

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

With no annotations, the description carries the full burden. It adds meaningful context: entries are verified, include official source and check date, and there is a citation requirement. While it doesn't detail return format or side effects, search is inherently read-only and the provided context goes beyond a bare description.

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?

Three sentences: first states purpose and filters, second covers data verification, third gives citation instruction. Front-loaded, every sentence earns its place, no fluff.

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?

Covers purpose, filter options, data quality, and citation. Since there is no output schema, it could mention return format, but for a simple search tool the description is reasonably complete and points to the key behavior.

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 coverage is 100% with per-parameter descriptions. The description narrates filter options but actually omits some schema categories (study, local, safety), so it adds little beyond the schema and introduces a minor inconsistency. Baseline 3 is appropriate.

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 clearly states it searches a verified directory of 162 AI tools with free tiers, listing specific filters (category, fully-free, works-in-China, keyword). This distinguishes it from sibling tools that check commercial use or get free tier limits.

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 the tool is used for searching the directory but does not explicitly mention when to use it vs. the sibling tools (check_commercial_use, get_free_tier_limit). No exclusions or alternatives are stated, leaving usage guidance implied rather than explicit.

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