SLM distillation recipe

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 the main side effect: optionally writing the axolotl YAML to artifacts/training. It also states the output content (axolotl/unsloth + vLLM recipe). It stops short of detailing overwrite behavior or whether the recipe is returned in-band, but the key behavioral trait is transparent.

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 contains the full purpose, scope, and side-effect condition with no 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?

The tool has no annotations and no output schema, so the description must supply output and invocation context. It explains the primary output and the optional file write, but it does not cover the 'custom' target path, the delivery mechanism of the emitted recipe, or when datasetFile is relevant, leaving some ambiguity.

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 schema already documents all three parameters. The description adds meaning for target by naming the supported models and for writeYaml by naming the artifacts/training path, but adds nothing beyond the schema for datasetFile. Baseline 3 is appropriate.

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 names a specific verb (emit), a concrete resource (LoRA distillation recipe), and the exact model targets (Qwen2.5-0.5B, ModernBERT-421M), plus the optional artifact write. This clearly distinguishes it from sibling jev_* tools such as jev_evaluate or jev_plan.

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?

No when-to-use guidance or alternative tool comparisons are provided. The description gives target-model context but does not tell an agent when to choose this over related tools or when the optional YAML write should be enabled.

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