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

With no annotations, the description carries the full burden of behavioral disclosure. It explains the computation method ('divides the officially published limits by that load'), the output categories (providers inside caps, exceeded, one-time credit duration, unknown figures), and clarifies a non-obvious behavior: providers with no published figure are reported as 'unknown rather than generous.' This is strong disclosure beyond basic function.

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 front-loaded with the core purpose and then details the computation and outputs. It is a single sentence plus an explicit usage recommendation. While a bit long, every clause adds useful information—no filler or repetition. It could be tightened, but it is well structured.

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?

The tool has no output schema, so the description must explain what the tool returns. It does so: it reports which providers stay inside caps, which are exceeded, how long one-time credit grants last, and which publish no figure. It also explains the input parameters. The only minor omission is not explaining the lang parameter, but given the tool's simplicity and the schema's enum, the description is sufficiently complete.

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%, so the baseline is 3. The description adds context by mapping 'expected calls per day' to requests_per_day and 'average tokens per call' to tokens_per_call, and clarifies that tokens include input plus output (though the schema already says this). It does not add meaning for the lang parameter, but the schema enumerates its options. The description provides marginal value beyond the schema.

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 the tool's purpose: 'Work out which verified free LLM API tiers can actually carry a given workload.' It identifies the specific verb ('work out'), the resource ('verified free LLM API tiers'), and the input needed ('expected calls per day and average tokens per call'). This distinguishes it from siblings like compare_free_tiers (which likely compares tiers) and get_free_tier_limit (which likely retrieves a single limit).

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 provides clear context for when to use the tool: when you need to assess workload fit against free tier caps. The final sentence, 'Use this instead of guessing whether a free tier is 'enough',' gives explicit usage guidance. It does not explicitly name alternative tools or state when not to use it, but the context is clear enough that an agent could infer appropriate usage.

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