sigma_update_data_model

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

It adds the critical behavioral trait of full replacement via PUT, which is not in annotations. Annotations already indicate non-read-only, and the description clarifies the mutation semantics.

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, no waste, directly states the operation and key mechanism.

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?

Given the output schema exists and annotations handle safety, the description covers the essential behavior. It could mention implications of full replacement (e.g., fields not in spec are removed), but that's inferable.

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 zero description coverage, but the description explains that the input is a JSON code representation, which maps to the 'spec' parameter. It doesn't elaborate on data_model_id or format details, so partial compensation.

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 action (update), the target (existing data model), and the method (full replacement via PUT), distinguishing it from create/get operations in the sibling list.

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?

It implies use when you need to replace an existing data model's full definition. It doesn't explicitly name alternatives like sigma_create_data_model, but the 'existing' and 'full replacement' language provides clear context.

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