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

With no annotations, the description carries the behavioral disclosure burden. It defines the exact computation, constrains t to 0-1, and documents the default of 0.5. It does not describe return type or edge-case handling, but for a simple deterministic math operation this 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 compact and front-loaded: the formula and inputs appear in the first sentence, and the optional/default detail follows immediately. Every word earns its place; only the redundant 'Lerp:' prefix is slightly wasteful.

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?

Given the tool's low complexity, empty input schema, and lack of output schema, the description provides enough for an agent to invoke it correctly: operation, inputs, range, and default. It could mention the return value explicitly or describe out-of-range behavior, but these are not material gaps.

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?

The input schema declares zero properties, so the description is the only source of parameter information. It names all three expected inputs—a, b, and t—and specifies t's constraint (0-1) and default (0.5), fully compensating for the empty 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 operation: linearly interpolate between values a and b by factor t, which is a specific verb-plus-resource definition. It does not explicitly differentiate itself from closely related siblings such as x402-lerp-value, x402-interpolate, or x402-inverse-lerp, so it misses the top score.

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 tells the agent what to supply ('Provide a, b, and optional t') and the default behavior, but it gives no explicit guidance about when to prefer this tool over alternatives like inverse-lerp, remap, or interpolate. Usage is implied rather than stated.

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