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, and it discloses nothing: not whether the computation is deterministic, what input format is expected, how lag affects the result, or what the response contains. A statistical computation tool with zero behavioral disclosure is unusable without guessing.

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?

At two words, this is not concise but under-specified — the same failure mode as the calibration's 'Process' example. Brevity here reflects missing content rather than disciplined editing.

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 two-parameter numerical tool with no annotations, no output schema, and no description of the algorithm, input encoding, or return value, the definition is completely inadequate. Nothing an agent needs in order to call it correctly is present.

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 nominally 100%, but the schema descriptions are placeholders ('Lag to process', 'Values to process') that name the fields without defining format — is 'values' a comma-separated string, JSON array, or whitespace-delimited? The description adds nothing to clarify this, so the baseline-3 credit for high schema coverage is not earned here.

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 is 'Autocorrelation: Autocorrelation' — a pure restatement of the tool name with no verb, no scope, and no distinction from statistical siblings like x402-correlation or x402-correlation-coefficient. An agent learns nothing about what computation is performed or on what kind of data.

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, no mention of alternatives (e.g. x402-correlation, x402-covariance, x402-acf-style siblings), and no preconditions. The agent has no basis for choosing this tool over adjacent statistics tools.

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