percentile

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states what the tool does (calculates percentiles) but does not describe how it behaves—e.g., how it handles edge cases (empty arrays, invalid percentiles), computational methods (linear interpolation), or output format. For a tool with no annotations, this leaves significant behavioral gaps.

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 concise and front-loaded, consisting of two clear sentences that directly state the tool's purpose. There is no unnecessary verbiage, and each sentence adds value by defining percentiles and providing domain context. It could be slightly improved by integrating usage hints, but it is efficiently structured.

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 complexity (statistical calculation with two parameters), lack of annotations, 0% schema description coverage, and no output schema, the description is incomplete. It does not address parameter details, behavioral traits, or output expectations, leaving the agent with insufficient information to use the tool effectively in context.

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 0% description coverage, so the description must compensate. It does not explain the parameters 'numbers' (e.g., that they are numeric strings representing the dataset) or 'percentile' (e.g., that it should be between 0 and 100). Without this semantic clarification, the agent may misuse the tool due to unclear parameter expectations.

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's purpose: 'Calculate percentiles of a dataset. Find the value below which a certain percentage of data falls.' It specifies the verb ('calculate'), resource ('percentiles of a dataset'), and includes a domain/category context. However, it does not explicitly differentiate from sibling tools (e.g., 'quartiles'), which are related but distinct statistical functions, so it falls short of a perfect score.

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 provides no guidance on when to use this tool versus alternatives. It mentions the domain ('statistics') and category ('general'), but offers no explicit context, prerequisites, or comparisons to sibling tools like 'quartiles' or 'comprehensive_stats'. Without such information, the agent lacks clear usage direction.

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