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

No annotations exist, so the description carries the burden. It usefully discloses the interpolation method ("linear interpolation between closest ranks"), which is genuine behavioral detail, but says nothing about determinism, error behavior, or the fact that it is a pure side-effect-free computation. Adequate but not rich.

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 that defines the operation and its algorithm with zero filler. Every clause 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?

For a pure numeric utility with no output schema (the return value is self-evidently a number) and one nested parameter object, the description plus schema cover what an agent needs to call it correctly. Missing only edge-case behavior such as empty lists.

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 has to compensate. It maps conceptually to both nested fields ("list of numbers" -> data, "Nth percentile" -> percentile), but adds no format rules beyond what the schema's minItems/maximum/minimum already enforce. Partial compensation.

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+resource ("Nth percentile of a list of numbers") and even specifies the computation method. Clearly distinguishable from siblings like stats_summary, stats_correlation, and stats_regression by being the single-percentile tool. It does not explicitly name those siblings, so a 4 rather than 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?

There is no guidance on when to use this versus stats_summary (which may also surface percentiles) or when not to use it. Usage is only implied by the math description, with no context, prerequisites, or alternatives.

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