percentiles

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

With no annotations, the description carries the burden of behavioral disclosure. It adds valuable context by revealing that the tool uses two methods and warns when n < 100 that p99 is just the maximum wearing a label. This goes beyond the minimum and provides a meaningful caveat, though it does not describe the exact return structure (likely covered by the output schema).

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 extremely concise at two sentences, front-loading the core function and adding a crucial warning. Every word earns its place with no fluff or repetition.

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?

Given the tool's simplicity (single parameter, output schema present), the description covers the essential behavior and an edge-case warning. It is largely complete, though it omits any usage guidance, which is a minor gap for a standalone utility.

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 schema has 0% description coverage for the only parameter 'nums'. The description does not explicitly explain what 'nums' should contain, though 'n' in the warning implies it is the count of numbers. This is insufficient compensation for the lack of schema documentation; the description adds minimal semantic meaning beyond the parameter name.

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 tool computes p50/p90/p95/p99 using both nearest-rank and linear interpolation, which is a specific and unambiguous purpose. It does not explicitly distinguish itself from sibling tools like calc_stats, but the percentile-specific focus is evident from the name and description.

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 description gives no explicit guidance on when to use this tool versus alternatives, and no mention of exclusions or prerequisites. The only implied usage is for calculating percentiles, which is too generic given the wide array of sibling calculation tools.

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