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verified-ai-free-tiers

get_free_tier_changes

List recently verified changes to AI free tiers - which vendor changed what and on which date. Vendors do not announce when they cut, rename or restructure a free tier; this log is accumulated from daily re-checks against official pages. Use this for "did anything change recently", "is my information still current", or when a user quotes a figure that may be stale.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
daysNoOnly changes within this many days. Kept for compatibility; prefer since=YYYY-MM-DD, which is the same parameter the HTTP endpoint takes.
langNoData language, default en
toolNoOnly changes for this tool slug or name
sinceNoOnly changes on or after this date (YYYY-MM-DD). Same contract as GET /api/changes?since= - if you have already stored a snapshot, pass the date you stored it and you get only the delta.

TDQS

A4.2/5.0
Behavior4/5

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

With no annotations, the description takes on the disclosure burden. It reveals that vendors do not announce changes and that the data comes from 'daily re-checks against official pages,' which informs the agent that the log is independently maintained and may include unofficial but verified findings. This adds valuable behavioral context beyond just listing changes.

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 two sentences: the first states the core function, the second explains the data collection method and provides concrete use cases. Every sentence earns its place with no filler, making it highly concise and well-structured.

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?

The description covers purpose, data source, and use cases. There is no output schema, but the description already conveys the returned information (vendor, change, date). It lacks explicit return-format details but is sufficiently complete for a simple list tool.

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?

All 4 parameters have descriptions in the schema, with the 'since' parameter explaining the delta contract. The tool description itself doesn't add parameter-level detail, but since schema coverage is 100%, the baseline of 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's function: 'List recently verified changes to AI free tiers - which vendor changed what and on which date.' This distinguishes it from siblings by emphasizing the verified-change log aspect, making it unique among tools like watch_free_tier_changes or compare_free_tiers.

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 lists use cases: 'did anything change recently', 'is my information still current', and 'when a user quotes a figure that may be stale.' It provides clear when-to-use guidance but does not explicitly name alternative tools or exclusions, so it's not a perfect 5.

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