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

With no annotations provided, the description carries the full burden of behavioral disclosure, and it discloses nothing at all. It does not state what data the tool operates on, whether it is a pure read/computation, what edge cases exist, or what the return value looks like. For a statistical function with no output schema, the absence of behavioral context is a critical gap.

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?

The two-word description 'Autocorrelation: Autocorrelation' is under-specification rather than conciseness - there is no information to be concise about. Nothing is front-loaded because there is no content, and every character is wasted on repetition. This mirrors the calibration example where a single-word tautology earned a 2.

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 statistical tool with no output schema and no annotations, the description must explain purpose and return behavior, and it explains neither. With heavily structured siblings (schema, stats, correlation) present, the agent cannot disambiguate or invoke this tool effectively. The definition is entirely inadequate for its context.

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?

The input schema is empty with 0 parameters and 100% schema coverage, so per the rubric the baseline is 4 - there is no parameter burden for the description to carry. The schema already fully documents that no arguments are required, so the description not adding parameter detail is not a deficiency 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 'Autocorrelation: Autocorrelation' is a pure tautology that simply restates the tool name in both the label and the content. It contains no verb, no resource, and no scope information - an agent cannot determine that this tool computes an autocorrelation statistic over a time series. This exactly matches the calibration example of 'Process'/'Process' scored at 1.

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 zero guidance on when to use this tool. The sibling list contains closely related tools such as x402-correlation, get_stats, and time-series tools (x402-ema, x402-rsi, x402-macd), yet the description never distinguishes autocorrelation from these alternatives. An agent has no basis for selecting this tool over any sibling.

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