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

The description transparently states the computation and requests a 'values array', which is useful with no annotations. However, it does not disclose edge-case behavior for empty or invalid datasets, nor what output format to expect.

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?

Extremely concise: two short sentences, with the definition front-loaded and the input instruction immediately following. Every word earns its place.

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?

There is a notable gap: the input schema has zero properties, yet the description says to 'provide values array' without explaining how that array should be passed. With no output schema and no annotations, the agent is left guessing about invocation mechanics and return shape.

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?

With 0 parameters, baseline is 4, and the description does add meaning by specifying that a 'values array' must be provided. It could be more explicit about array element types, but it compensates for the empty schema.

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 clearly states the verb-resource pair: 'Calculate the midrange of a dataset' and gives the precise formula ('the average of its minimum and maximum'). This distinguishes it from sibling statistical tools like median, midpoint, and midhinge.

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?

The formula implies the use case, but the description gives no explicit guidance on when to choose midrange over siblings such as median, mean-of, or get_stats. No alternatives or exclusions are mentioned.

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