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

No annotations are provided, so the description must disclose behavioral details, but it only restates the statistical definition. It does not explain how data is supplied, what the tool returns, whether it is read-only, or whether any state/context is required.

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 and front-loaded, but it largely repeats the tool name in definitional form. It is not padded or verbose, yet it does not add much operational value beyond the 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 parameters, and no output schema, the description carries the burden of explaining the invocation contract. It only gives a definition, leaving ambiguous how the agent should call the tool or interpret its result.

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 has zero parameters, so there are no parameter semantics to document; per baseline, zero-param tools receive a 4. The description adds no parameter-level detail, but none is needed 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 clarifies that the tool is about median absolute deviation, a specific statistical concept, so an agent can identify the general purpose. However, it lacks an explicit action verb such as 'computes' and does not distinguish itself from related siblings like x402-mean-abs-deviation or x402-median.

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 guidance on when to use this tool versus the many related statistical tools. No use cases, exclusions, or alternative tool references are present in the description.

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