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

The description adds substantial behavioral context beyond the annotations: it explains that the returned script reads the file, aggregates rows to group counts, drops identifying columns, prints decision cards, writes hiring_fairness_result.json, and never sends candidate data to the server. It also reinforces the group-level-only constraint and no individual scoring or ranking.

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 front-loaded with the large-file and sandbox use case, then moves efficiently through return behavior, parameter passing, and privacy constraints. Every sentence adds operational value without 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 complexity, annotations, output schema, and fully documented parameters, the description is complete enough for an agent to select and invoke it correctly. It covers the large-file routing, script behavior, group-level output constraint, and data privacy handling.

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 coverage is 100%, so the parameter baseline is 3, but the description adds meaning by explaining that selection_ratio, annual_salary, reference_group, and other inputs are passed through in params exactly as hiring_fairness takes them. It also clarifies that data_path is a CSV/TSV/JSON file in the sandbox.

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 a specific action, returning a complete numpy-only Python script, and clearly scopes it to hiring files over 5,000 rows or ATS exports already in the sandbox. It also distinguishes this path from the sibling hiring_fairness tool by explaining it uses the same engine but handles large local files.

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 a clear usage condition: files over 5,000 rows or ATS exports already in the code sandbox. It does not explicitly say to use hiring_fairness for smaller files, but the threshold and sibling reference make the intended routing largely clear.

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