Auto-dataset labeler ($0/row on local backends)

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

With no annotations, the description carries the full burden and does disclose a key side effect: it writes a JSONL file into artifacts/datasets and processes in a 'fan-out pass' (parallel execution). It does not, however, disclose that writeFile defaults to true, what happens when writeFile=false, or whether existing files are overwritten.

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?

One 24-word sentence packs the action, method, output format, destination, and purpose with zero filler. The cost context in the title complements rather than duplicates the description.

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?

The description covers the core call flow and write destination, and all parameters are schema-documented, so an agent can mostly invoke it correctly. Without an output schema or annotations, key behaviors remain undisclosed: the default writeFile=true side effect, return behavior when writeFile=false, and overwrite semantics.

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 100%, so the baseline applies; the schema already documents all five parameters. The description adds minor context by framing the enum modes as 'Jev primitives' and naming the exact write destination, but it does not meaningfully extend parameter understanding beyond 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 a specific verb ('Label'), resource ('raw rows'), method ('Jev primitives'), and output ('JSONL dataset into artifacts/datasets for distillation'), which clearly identifies the tool's function. It does not explicitly contrast with siblings like jev_preference_pairs or jev_evaluate, which also produce datasets, so it stops short of full sibling differentiation.

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 phrase 'for distillation' implies the use case, and the title's '$0/row on local backends' hints at cost as a decision factor. However, the description never explicitly states when to choose this over siblings (e.g., jev_preference_pairs or jev_rerank) or when not to use it.

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