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

With no annotations, the description carries the full burden of behavioral disclosure, and it only says 'Compute', offering no detail on input format, which parameter pair to supply, output representation (decimal vs percentage), or edge-case behavior (e.g., empty arrays, constant yTrue). The presence of two apparently redundant parameter pairs (yPred/yTrue vs actual/predicted) is a behavioral ambiguity the description never resolves.

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 tight sentence with zero waste; the verb is front-loaded and the parenthetical 'coefficient of determination' adds precision. The only minor flaw is the redundant 'R2 Score:' label that echoes the tool name.

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, four optional params, and dual ambiguous parameter pairs, an agent cannot determine how to call this tool correctly — specifically which inputs to provide and in what string format. The near-duplicate sibling x402-r-squared compounds the ambiguity, and the description does nothing to resolve it. A tool this simple should still state input expectations and return format.

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 100%, but the parameter descriptions are pure tautologies ('YPred to process', 'Actual to process') that add no meaning beyond the parameter names, so the schema is not doing the heuristic heavy lifting the baseline-3 assumes. The description itself contributes nothing about parameter semantics, and it never clarifies whether yPred/yTrue and actual/predicted are alternate pairs, aliases, or all required.

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: 'Compute R² (coefficient of determination)'. This clearly identifes the statistical metric being calculated. However, it does not distinguish itself from the near-duplicate sibling tools x402-r-squared and x402-adjusted-r-squared, so it falls short of full sibling differentiation.

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?

Provides no guidance on when to choose this tool over the many statistical siblings (r-squared, adjusted-r-squared, mean-squared-error, root-mean-squared-error, correlation). No context, no exclusions, no alternatives are mentioned. The score is 2 because it is simply absent rather than misleading.

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