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

No annotations are provided, so the description carries the full behavioral burden. It usefully discloses the equal-length input constraint, which the bare schema (two strings, 0% coverage) does not, but it omits error behavior, acceptable input encoding, and the shape of the computed result.

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?

One efficient sentence, front-loaded with the core operation and input constraint. The free-trial wallet blurb is marketing filler but is short and does not obscure the purpose.

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 annotations, no output schema, and 0% parameter coverage, the description should specify input encoding and the return value (a single statistic). It leaves the format of the two required arrays entirely unstated, so an agent cannot confidently construct the call.

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 names both parameters ('observed and expected arrays') but does not specify the encoding (raw string? JSON array? comma-delimited?) despite the schema declaring them as strings. That format ambiguity is a real gap for a 0%-coverage 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?

States a specific verb and resource ('Computes the chi-square statistic') and specifies the input shape (equal-length observed and expected arrays), so an agent can tell it apart from sibling statistics tools like x402-t-statistic or x402-r-squared. It does not explicitly name a sibling for differentiation, keeping it just below a 5.

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 or when-not-to-use guidance and no alternatives named among the many statistical siblings. The only usage signal is the implicit constraint that inputs must be equal-length, which is a precondition rather than routing guidance.

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