remove_catalog_model

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

With no annotations, the description carries the full burden. It discloses the key limitation (cannot remove if in use) and the return behavior ('Confirmation or error if model is in use'). This goes beyond the bare action and informs the agent of likely failure modes.

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 appropriately sized, front-loaded with the main purpose, and uses a clear docstring structure. Every line adds useful information without verbosity.

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 single-parameter tool with an output schema, the description covers purpose, constraint, parameter, and return. It does not explain domain-specific concepts like 'fallback chain' or whether the operation is irreversible, but these are minor for the tool's simplicity.

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 0% description coverage, so the description must compensate. It does: 'name: Name of the model to remove' clearly explains the single parameter's purpose. Though minimal, it is sufficient for a simple string parameter.

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 exactly what the tool does: 'Remove a model from the catalog.' This is a specific verb+resource pair and clearly distinguishes from sibling tools like add_catalog_model or update_stage_model, which handle other operations.

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 provides clear context for when removal is appropriate, and explicitly states a 'when-not' condition: 'Cannot remove a model that is currently set as a stage default or in a fallback chain.' It does not name alternatives, but the purpose and constraint are clear enough for an AI agent to select it correctly.

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