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 delivers only a mathematical definition. It does not state what inputs the computation expects, what the return value looks like, how errors are ordered (actual vs. predicted), or how edge cases like mismatched array lengths are handled. The computation itself is implied but no operational behavior is disclosed.

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 only six words and contains zero fluff, which is efficient. However, it is so thin that it borders on under-specification rather than disciplined conciseness. It lacks a structured presentation (e.g., what the tool inputs, what it returns), so while there is no wasted text, there is also almost no content to structure.

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 tool with zero parameters in schema, no output schema, and no annotations, the description is the only source of guidance — and it is a six-word definition. The agent cannot determine how to supply the data, what result format to expect, or how this differs from the many sibling error/statistics tools. The description is not adequate for reliable invocation.

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 is nothing for the description to clarify about parameters — per the rubric, 0 params earns a baseline of 4. Notably, the empty schema is itself a gap: a mean-absolute-error tool must receive data somehow, and neither the schema nor the description explains how that data is provided, but this is not a parameter-semantics failure.

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 Absolute Error: Mean of absolute error' is essentially a tautology — it restates the tool name, then gives the dictionary definition of the term. It does clarify that the metric is the mean of absolute error values, but it never states what the tool does in operational terms (e.g., computes a metric from two arrays). With siblings like x402-mean-squared-error, x402-root-mean-squared-error, and x402-mean-abs-deviation nearby, this provides no 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?

No guidance whatsoever on when to use this tool versus the many closely related statistical siblings (x402-mean-abs-deviation, x402-mean-squared-error, x402-root-mean-squared-error, x402-standard-error). An agent choosing between MAE, MSE, and RMSE gets no decision support. No context, use cases, or exclusions are provided.

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