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verified-ai-free-tiers

by io.github.f-tiger

audit_ai_stack

Audit several AI tools at once: for each one return its verified free-tier limit, whether the free-tier output may be used commercially, any recently verified change to its allowance, and whether the vendor publishes a figure at all. Use this when a user names the stack they rely on ("I use Cursor, Runway and Kimi") - it answers in one call what would otherwise take four lookups per tool, and it surfaces the two things people miss: an allowance that quietly changed, and a tool whose ceiling is simply unknown because nobody publishes it.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
langNoData language, default en
toolsYesTool slugs or names, e.g. ["cursor", "runway", "kimi"]

TDQS

A4.5/5.0
Behavior4/5

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

With no annotations provided, the description carries the burden of behavioral disclosure. It reveals that the tool returns verified data and highlights the two common surprises (changed allowances and unknown figures). It does not mention error handling, rate limits, or data source freshness, but covers the key behavioral nuances of the audit.

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?

The description is compact yet information-dense. It front-loads the purpose with a colon list, then provides usage context and value-add in a second sentence. No wasted words; every sentence earns its place.

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 tool's complexity (multi-tool audit), the description effectively explains what it returns (four data points per tool) and when to use it. Since there is no output schema, the description adequately substitutes by listing the output fields, making the tool's behavior transparent and complete.

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 100%, so parameters are already well-documented. The description adds an example of tool names ('Cursor, Runway and Kimi') but does not add extra semantics beyond the schema, which is acceptable given the 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 audits several AI tools at once and enumerates the four specific data points it returns (free-tier limit, commercial use, changes, publication status). It distinguishes itself from siblings by being a multi-tool, multi-aspect aggregate, unlike single-purpose siblings like check_free_tier_claim or get_free_tier_limit.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly says 'Use this when a user names the stack they rely on' and contrasts with the alternative of 'four lookups per tool'. This gives concrete when-to-use guidance and differentiates from sibling tools that handle individual checks.

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

A4.4/5.0
Disambiguation5/5

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.

Naming Consistency5/5

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.

Tool Count5/5

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.

Completeness5/5

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.

Resources