mlflow_registered_models_get

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

Annotations already set readOnlyHint=true, so the agent knows this is a read-only operation. The description adds the HTTP method and endpoint, which is technical but does not disclose additional behavioral traits like permissions or returned fields.

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 extremely concise (one sentence) and front-loaded with the core purpose. The inclusion of the HTTP URL is slightly redundant but does not harm conciseness.

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 presence of an output schema, the description does not need to detail return values. However, it omits any mention of prerequisites (e.g., model must exist) or behavior when model not found, which a complete description should include.

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 covers the single parameter 'name' with a clear description ('Registered model name'). The description adds no further parameter meaning beyond what the schema already provides.

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 explicitly states 'Get a registered model', which is a specific verb+resource pair. It clearly distinguishes this retrieval operation from sibling tools like mlflow_registered_models_list (list all) and mlflow_registered_models_create (create new).

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 usage guidelines are provided. The description lacks any guidance on when to use this tool versus alternatives (e.g., mlflow_registered_models_list for multiple models, or mlflow_registered_models_get_by_name).

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