run_svr

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

No annotations provided, so description carries full burden. It mentions fitting and returning results but omits computational cost, whether this caches models, expected kwargs structure, or side effects of the training process.

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?

Single sentence of nine words is efficiently structured, but results in under-specification for a complex ML tool with opaque parameters. Every word earns its place, yet the brevity creates documentation gaps.

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 (reducing description burden for returns), the tool critically lacks input documentation. For an ML training operation with completely undocumented kwargs, the description fails to explain data requirements, hyperparameter options, or model configuration.

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 0% (the 'kwargs' parameter has no description). The description fails to compensate by documenting what keys/values are expected in kwargs (features, target, hyperparameters), leaving the single required parameter completely undocumented.

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?

Clear verb ('Fit') and resource ('Support Vector Regressor'), identifying this as a specific ML algorithm among siblings. However, it does not clarify when to choose SVR over alternatives like run_random_forest or run_xgboost.

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 guidance provided on when to use this regressor versus sibling algorithms (run_random_forest, run_xgboost, etc.) or prerequisites like data preprocessing needs.

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