transform_set_function

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

With no annotations, the description carries the full burden. It discloses that the operation writes to the 'Function' child field and changes the Function type, but it does not describe overwrite implications, validity requirements, potential errors, or reversibility.

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?

Three dense, front-loaded sentences with no filler: purpose, side-effect detail, and the key exception. Every sentence earns its place.

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?

For a simple two-parameter setter this is largely adequate, and the Optimize exception is helpful. But it would be more complete with a pointer to transform_get_funcdict for valid Function values and a note about transform prerequisites.

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 describes both parameters with 100% coverage, so the baseline is 3. The description adds useful example values and the Optimize exception, but it does not clarify transform_path syntax or list the set of acceptable Function values.

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 names the exact action and resource: 'Set the Transform's Function type,' and gives a concrete example ('PythonLocal' to 'PEL'). It is clearly distinct from siblings like transform_get_function and transform_create.

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 says to use transform_create with transform_type='Optimize' for Optimize type, which is a clear alternative. However, it does not mention transform_get_funcdict for discovering valid Function values or provide broader when-to-use context.

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