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

With no annotations, the description carries the full behavioral burden, and it delivers: payment requirement, one-payment model, translation pipeline, LLM vs MADLAD-400 fallback tiers, uneven rare-language quality, published per-language quality, and pricing structure. It does not specify whether the answer returns synchronously or via a job/result mechanism, which is a minor gap.

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 long but information-dense and front-loaded with the core action, followed by important operational and pricing details. Every sentence contributes substantive context, though the structure is a bit sprawling with several clauses in quick succession.

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?

For a paid endpoint with no output schema and meaningful dependencies, the description is quite complete: it covers the payment prerequisite, language coverage, fallback behavior, quality variability, and cost model. It could additionally explain the answer return format or whether the call is asynchronous, but the core information needed to invoke it correctly is present.

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%, so the baseline is 3, but the description adds meaningful cross-parameter semantics: the create_payment quote must match the chosen language and tier, the model choice must match that quote, and the rare-language path costs more. It also highlights that unsupported languages are rejected before payment, reinforcing the language parameter's validation behavior.

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?

States a specific verb ('ask'), a specific resource (frontier AI), and the distinctive same-language behavior in the first sentence. It clearly differentiates itself from translation-only siblings like translate_text and translate_rare_language by framing the tool as question-answering, not translation.

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?

Clearly establishes when to use the tool (ask a question in your own language and get a frontier-model answer back in that language) and gives a strong prerequisite: create_payment must be called with toolName='multilingual_ask' and the intended language. It does not explicitly name sibling alternatives to exclude, so it stops short of a 5.

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