get_registered_models

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

With no annotations present, the description carries the full transparency burden. It correctly signals a read-only list operation and identifies the returned data as versions and stages, but it does not mention pagination, authentication needs, or empty/error behavior.

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?

One short sentence contains the action, resource, scope, and output data. It is front-loaded and free of filler or redundant phrasing.

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 zero-parameter tool with no output schema, the description provides the core subject and key output fields, but not the exact result structure or edge-case behavior. It is adequate for selecting the tool but leaves some context gaps for an agent that must rely purely on the description.

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 has zero parameters and an empty properties object, so there are no parameters for the description to explain. The baseline score for a no-parameter tool is appropriate here.

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 uses a specific verb ('List') and names the exact resource: registered ML models in the MLflow model registry, including versions and stages. This makes it clearly distinguishable from sibling tools like get_mlflow_experiments, which target a different MLflow resource.

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 provides no guidance on when to use this tool versus alternatives such as get_mlflow_experiments. It states what the tool does but gives no context, exclusions, or alternative-selection criteria.

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