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

With no annotations, the description carries the full burden, and it does disclose two behavioral facts: lag defaults to 1, and there are 5 free trial calls per signed wallet. It says nothing about return shape, precision, minimum sample size, or error behavior for short/invalid arrays.

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?

A single front-loaded sentence delivering the definition, the default, and the pricing note with no padding. The trailing emoji/promo text is mild noise but does not bury the core meaning.

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 simple two-parameter statistical function with no annotations and no output schema, the description covers the operation and the lag default but leaves return values, input serialization, and edge-case behavior unstated. Adequate but with clear 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?

Schema description coverage is 0%, so the description must compensate; it does explain that 'values' is an array and that 'lag' controls the lag-k offset with a default of 1. However, it omits the wire format for both string-typed parameters (e.g. how the array is serialized) and any bounds on lag.

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 and resource: 'Computes the lag-k autocorrelation coefficient of a values array', with the default lag called out. It is distinguishable from the nearby x402-correlation and x402-correlation-coefficient siblings, though it never names them 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?

No when-to-use guidance, no conditions or exclusions, and no reference to alternatives such as x402-correlation or x402-covariance. Usage is only implied by the mathematical definition.

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