mlflow_registered_models_update

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

The description confirms it's a mutation (POST) and annotations already indicate readOnlyHint=false. It adds no additional behavioral traits such as side effects, permissions, or reversibility, but it doesn't contradict annotations.

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 very short (one sentence) but includes the HTTP endpoint. While concise, it could be more informative without becoming verbose.

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 that output schema exists and annotations are present, the description still lacks important context about what fields can be updated, constraints (e.g., name uniqueness), and the expected behavior of the new_name parameter.

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 description coverage is low (33%), with only 'name' described. The tool description does not add meaning to 'description' or 'new_name', leaving the agent to infer from parameter names alone.

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 'Update a registered model' which is a specific verb and resource. It also provides the HTTP endpoint. However, it does not differentiate from sibling tools like 'mlflow_registered_models_create' beyond what the name implies.

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?

No guidance on when to use this tool versus alternatives (e.g., create or update model versions). No prerequisites or exclusion criteria are mentioned.

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