AxModelFitter_generate_code

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

With no annotations provided, the description carries the burden. It discloses that the tool returns executable code and an explanation, and that the code must be executed separately, which is a key behavioral trait. However, it doesn't state potential limitations (e.g., code correctness, dependency requirements) or explicitly note that it does not execute the code itself, though this is implied.

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 three concise sentences, front-loaded with the main purpose. Every sentence adds value: what it does, what the user provides, and what it returns. No filler or redundant content.

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 description covers the core essentials: purpose, input (problem description), output (code and explanation), and execution next step. It lacks details on error handling or edge cases, but for a one-parameter code-generation tool with no output schema, it is sufficiently complete for an agent to use 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?

The schema has 100% coverage with a clear description for the single parameter (problem_description). The tool description essentially repeats the schema wording ('Describe the model, the data, and what you want to fit') without adding new semantic detail, so it meets the baseline for high schema coverage.

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 states a specific verb ('Generate Python code'), a specific resource ('fit a parametric model'), and the library ('ax_core.model_fitter'). It clearly distinguishes itself from execution tools like AxModelFitter_execute_code and legacy fitting tools by implying code generation rather than direct fitting.

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 explicitly instructs that the code must be executed separately using the execute_code tool, providing a clear workflow. However, it does not mention alternative tools like AxModelFitterLegacy_fit_model for direct fitting or when to prefer this over other generate_code tools.

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