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

The annotations only declare it as a non-read-only, non-destructive, closed-world write. The description adds genuinely useful behavioral context beyond that: the process is deterministic, candidates are admitted only if they are verified successes on their own checks, and it has no training cost or spend. This is a meaningful disclosure for a mutation tool, though rate limits and permission requirements remain unaddressed.

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?

Two tight sentences with the purpose and mechanism front-loaded and the cost/no-training clarification placed last where it matters. Dense but no wasted phrasing; the breakdown of steps ('extracts... clusters... admits...') is arguably more detail than a selector needs, keeping it just short of 5.

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 four-parameter mutation tool with full schema coverage and no output schema, the description pairs the purpose with the non-training/no-cost guarantee, which is what an agent most needs to avoid misfiring. It stops short of describing the returned handle (e.g., a dataset id) or next steps, but no output schema exists so this is largely acceptable.

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 all four parameters, including the superadmin-only customerId and the splitConditionKeys default/max, are already documented in the schema. The description's mention of 'instruction version × conditions' loosely gestures at the splitting concept but adds no syntax or constraint detail beyond the schema, so baseline 3 applies.

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 verb (curate/create) and resource (a dataset from an agent's completed runs) and describes the concrete mechanism: extracting candidates, deterministic clustering by finding type × instruction version × conditions, and admitting verified successes. This clearly distinguishes it from read-side siblings like get_dataset and list_datasets.

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?

Usage is implied rather than stated: it's the tool that builds a dataset from completed runs, and 'Trains nothing and spends nothing' rules out a training interpretation. However, it never says when to prefer this over siblings like list_dataset_examples, freeze_dataset_version, or bind_dataset_version, nor does it name any prerequisite or exclusion.

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