AxModelFitterV2_generate_code

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

With no annotations provided, the description must handle behavioral disclosure. It correctly states the tool returns executable code and an explanation, and mandates separate execution. However, it does not mention side effects, permissions, or safety characteristics (e.g., read-only nature).

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?

Two sentences covering purpose, usage, and dependency on another tool. No redundant information; every sentence adds value. Front-loaded with the core action.

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 input parameter and the follow-up action (execute_code). It lacks details about return format, error handling, or example usage, but given the simple single-parameter input and no output schema, it is mostly sufficient for an agent to invoke the tool 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 coverage is 100% for the single parameter. The description adds that the parameter is a natural language description of the model, data, and fitting goal, which largely echoes the schema description. It provides no additional syntax or constraints beyond the schema.

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 tool generates Python code to fit a parametric model using a specific JAX library, with a specific verb ('generate') and resource ('code'). It distinguishes from siblings like AxModelFitter_fit_model (which actually fits the model) and AxArgmin_generate_code (for argmin).

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 tells the user to describe the model, data, and fitting goal, and mentions that the code must be executed separately with execute_code. However, it does not explicitly contrast with alternatives (e.g., when to use this vs. AxModelFitter_fit_model), relying on implied context.

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