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

No annotations exist, so the description carries the full burden. It usefully discloses the returned artifacts (breakeven volume, cost comparison, recommendation), which is the key behavior for a tool with no output schema, but it never states that the tool is a stateless computation, nor mentions defaults or assumptions for the optional inputs.

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?

Two sentences, zero waste, with the use-condition front-loaded and the output summary second. Appropriately sized for a single-purpose calculator.

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?

With no output schema and no annotations, the description must convey returns, which it does. Six parameters are all documented in the schema. Only the omission of computation assumptions (defaults, statelessness) keeps it from a 5.

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 schema already documents all six parameters and their defaults (utilization 60%, overhead 40%). The description adds no parameter meaning beyond that, making the baseline 3 correct.

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 pairs a clear trigger ('deciding between API usage and self-hosted GPU inference at a given volume') with a specific output set (breakeven token volume, cost comparison, go/no-go recommendation). This distinguishes it well from the cost-calculator siblings, though it never names an alternative tool explicitly.

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 gives an explicit when-to-use condition scoped to a decision context, which is stronger than most calculators. It offers no when-not guidance or named alternative, 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.