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

With no annotations, the description carries the full behavioral burden. It usefully discloses the algorithm intent ('transparent median', i.e., plain median, not a trimmed/calibrated estimate) and a negative scope (no percentile claims), but says nothing about output format or error behavior for invalid input.

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?

A single front-loaded sentence with the verb and resource first, then the scope caveat. Every clause earns its place with no filler.

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 small computation tool with no output schema and no annotations, the description is adequate on input and scope but omits what is returned (the median value) and how invalid or out-of-range input is handled. Enough to call it, but not fully complete.

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?

Schema description coverage is 0%, so the description must compensate for the single parameter, and it does: it conveys count (five), unit (milliseconds), and qualifying semantics (user-supplied, valid, browser-observed). This meaningfully enriches the bare 'trials_ms' array.

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?

States a specific verb (Calculate) and resource (median of five user-supplied reaction times in ms), so the operation is unambiguous. It does not, however, reference the sibling find_reaction_test or otherwise differentiate the two, keeping it below a 5.

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?

Usage is only implied by the computation itself; there is no explicit when-to-use, no named alternative, and no prerequisites. The 'no calibration or population percentile claims' clause hints at an exclusion, but it reads as a scope disclaimer rather than routing guidance.

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