mlflow_model_versions_get

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

Annotations already declare readOnlyHint=true, so the read-only nature is clear. Description adds no further behavioral context (e.g., authentication, rate limits, or side effects). With annotations present, the description provides minimal added value.

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?

Description is a single, short sentence. It is front-loaded with the core action. The included API endpoint is slightly redundant but not overly verbose. Efficient.

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 existence of an output schema (not shown), the description does not need to detail return values. However, it lacks context on how this tool fits among siblings (e.g., 'use this to get details of a specific version by name and version'). Adequate for a simple get operation but could be more complete.

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?

Input schema covers both parameters (name, version) with descriptions. Description does not add any additional meaning beyond the schema. Expectation met at baseline.

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?

Description clearly states 'Get a model version' with verb and resource. Includes API endpoint for reference. However, it does not differentiate from sibling tools like mlflow_model_versions_list, which also retrieves model versions.

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 vs alternatives such as mlflow_model_versions_list. No prerequisites or exclusions mentioned. Agent receives no context for selection.

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