run_xgboost

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

With no annotations provided, the description carries the full disclosure burden. It adds valuable behavioral context by specifying the tool returns 'feature importances' and identifies it as a 'Regressor' (not classifier). However, it omits other behavioral details like side effects, memory/computation constraints, or failure modes.

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, efficiently structured sentence with the action verb front-loaded. Every word earns its place with no redundancy or tautology.

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?

Despite having an output schema (which covers return values), the description inadequately documents inputs for this complex ML tool. With 0% input schema coverage, the opaque 'kwargs' parameter requires explanation that is completely absent, making the tool difficult to invoke correctly.

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 0% (the single 'kwargs' parameter has no description). The description fails to compensate by explaining what data or hyperparameters should be passed to this opaque parameter, though 'Fit' implies data is required. This is a significant gap for invoking the tool correctly.

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 provides a specific verb ('Fit') and resource ('XGBoost Regressor'), clearly identifying this as a model training tool. It distinguishes from siblings by naming the specific algorithm (XGBoost vs Random Forest, SVR, etc.), though it lacks explicit comparative guidance on selection criteria.

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 select this tool versus sibling ML algorithms (run_random_forest, run_svr, etc.). It does not indicate appropriate use cases for XGBoost specifically or prerequisites like data format requirements.

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