finetune_model

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions 'Unsloth optimizations' but doesn't explain what this entails (e.g., performance improvements, memory efficiency). Critical behavioral traits like computational cost, time requirements, potential data destruction, or output format are omitted. The description adds minimal context beyond the basic action.

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 a single, efficient sentence with zero waste. It is front-loaded with the core action ('fine-tune a model') and includes a relevant detail ('with Unsloth optimizations') that adds value without verbosity. Every word 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?

Given the complexity of a fine-tuning operation (12 parameters, no annotations, no output schema), the description is incomplete. It lacks information on behavioral aspects (e.g., runtime, resource usage), output (what is returned), and usage guidelines. The high parameter count and absence of structured support fields make this inadequate for such a complex tool.

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 schema already documents all 12 parameters thoroughly (e.g., 'model_name', 'dataset_name', 'lora_rank'). The description adds no additional meaning about parameters, such as typical values, constraints, or relationships between them. Baseline score of 3 is appropriate as the schema does the heavy lifting.

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 clearly states the action ('fine-tune') and resource ('a model') with a specific optimization method ('with Unsloth optimizations'). It distinguishes from siblings like 'export_model' or 'generate_text' by focusing on training rather than inference or export. However, it doesn't explicitly differentiate from 'load_model' in terms of when to use each.

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 no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites (e.g., needing a pre-trained model and dataset), when not to use it (e.g., for inference tasks), or refer to sibling tools like 'list_supported_models' for model selection. Usage is implied but not explicitly stated.

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