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

No annotations exist, so the description carries the full burden and does add useful behavioral detail: output rounding (6 decimals for slope/intercept, 4 for r2), the two-point minimum, and the 5-free-call wallet gating. It omits error behavior for unequal-length or malformed arrays and how x/y relate to xs/ys.

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?

It is a single front-loaded sentence, but the leading 'Linear Regression:' restates the tool name and the emoji plus trial-call pricing add clutter without aiding invocation.

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 four undocumented parameters, the description should resolve the x/y vs xs/ys ambiguity and describe error handling. As written, an agent cannot confidently determine which parameters to supply.

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% across all four parameters, so the description must compensate. It only mentions 'equal-length x and y arrays,' leaving the xs and ys parameters entirely unexplained and their relationship to x/y (aliases or alternatives?) ambiguous.

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 ('Fits a least-squares line') and specifies return values with precision. It distinguishes itself from regression-adjacent siblings such as x402-r-squared or x402-interpolation, though it does not explicitly name an alternative.

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?

There is no when-to-use guidance or routing to alternatives among the many stats siblings. The only usage constraint given is 'needs two points,' which is a precondition rather than situational guidance.

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