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

With no annotations provided, the description carries the full disclosure burden. It reveals that the calculation is deterministic, includes stated overhead, checks against the live order book, and refuses to invent throughput values. It does not describe the output format or pagination behavior, so it is not fully transparent.

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 dense but every sentence earns its place: the purpose question, the formula, the accepted input modes, precision options, throughput behavior, and assumptions disclosure. The most important information is front-loaded.

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 tool with eight parameters, no annotations, and no output schema, this description is unusually complete: it states required input modes, formula components, optional enhancements, and output ordering. Still, it leaves limit and offer_class undocumented and does not describe the returned offer fields, requiring some agent inference.

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 0%, so the description must compensate. It explains model vs params_b, precision choices, context/batch via the KV-cache formula, and tokens_per_s semantics. However, offer_class and limit are never explained, and there are no enums in the schema to fill that gap.

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 opens with a direct, specific question about model fit and rental cost, then states exact behavior: deterministic VRAM arithmetic compared against the live book with cheapest fitting offers first. This clearly differentiates it from sibling tools like search_offers, get_offer, and rent_offer.

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?

It provides clear invocation guidance: pass a known model slug or params_b, choose precision from fp16/int8/int4, and optionally pass your own measured tokens_per_s to enrich offers with usd_per_m_tokens. It does not explicitly name alternative tools or state when not to use it, 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.