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, yet it says nothing about how the tool operates, what input it consumes, what output it produces, or side effects. The text adds zero behavioral transparency beyond the bare name.

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 description is short but this is under-specification rather than effective conciseness. It is a one-line rephrasing of the tool name and does not earn its place by conveying any actionable information.

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 an empty input schema, the description needed to explain the tool's input, output, and relation to its many sibling tools. It provides none of this, making it completely inadequate for an agent to select or invoke the tool correctly.

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 declares zero parameters, so per the rubric baseline this dimension is 4; there are no parameter semantics for the description to document. However, the description also fails to clarify what data the tool operates on, which is more appropriately penalized under contextual completeness.

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 'Mean Abs Deviation: Mean of abs deviation' merely restates the tool's name in words; it defines the statistical concept rather than describing what the tool does for the agent. It provides no operational verb or resource and is indistinguishable from closely related siblings like x402-median-absolute-deviation or x402-mean-absolute-error.

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 guidance is given on when to use this tool versus alternatives such as x402-median-absolute-deviation or x402-mean-of. There is no mention of input requirements, exclusions, or the context in which mean absolute deviation is preferred, leaving the agent to infer usage entirely from the mathematical term.

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