run_linear

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

Without annotations, the description carries the full burden. It discloses that the tool returns 'coefficients and residuals,' hinting at the output structure and read-only analysis nature. However, it lacks details on computational complexity, convergence behavior, or memory requirements typical for model fitting operations.

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, front-loaded with the action verb, no redundant phrases. Appropriate brevity for the information provided.

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 (covering return values), the tool description inadequately addresses the complexity of a statistical modeling operation. The opaque 'kwargs' parameter combined with lack of annotations leaves critical usage context undocumented.

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?

The input schema has 0% description coverage with a single opaque 'kwargs' parameter. The description fails to compensate by documenting expected arguments (features, target data, fit_intercept, etc.), making the tool essentially uninvokable without external documentation.

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 ('Linear Regression'), clearly identifying the statistical operation. However, it does not differentiate from sibling regression tools (run_bayesian_ridge, run_svr) regarding when linear regression is the appropriate choice.

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 tool versus alternatives like run_random_forest or run_xgboost. No prerequisites or data format requirements are mentioned, despite this being a statistical modeling operation.

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