calculate_percentile_rank

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

No annotations are provided. The description only mentions the return range (0-100) but does not disclose read-only nature, authentication requirements, or the fact that the tool expects pre-computed counts rather than a full dataset. This omission is significant for correct usage.

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 brief (two sentences) but includes inaccurate information about the input. Conciseness is hindered by the misleading content, making it less effective.

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 mismatch between the description and actual parameters, the tool's usage is poorly explained. The description fails to clarify that the tool requires pre-counted values (values_below and total_values) rather than a dataset, which is critical for correct invocation. The output is partially covered.

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 already describes each parameter clearly (value, values_below, total_values). The description adds no additional value and misleads by referencing a 'dataset' parameter that does not exist, contradicting the 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 states the tool computes percentile rank of a value within a dataset for benchmarking, which is specific and clear. However, it mentions a 'dataset' input that does not correspond to the schema, introducing potential confusion.

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 a use case (benchmarking scores or salaries) but does not differentiate from the many sibling calculate_ tools or specify when not to use it. The reference to list_bundles is vague and does not clarify alternatives.

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