mlflow_model_versions_list

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

Annotations indicate readOnlyHint=true, confirming no modifications. The description adds no behavioral details such as pagination behavior, ordering defaults, or that results are limited to model versions the caller can access. It merely restates the endpoint.

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 only one sentence, which is concise but lacks important structural details. It front-loads the purpose but omits parameter context and usage notes.

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 the tool has 4 parameters with zero schema descriptions, the description falls short. It does not explain how to use the tool effectively for searching model versions, despite having an output schema that documents return values.

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 0%, yet the description provides no explanation of the four parameters: filter, max_results, order_by, page_token. An AI agent would have no guidance on constructing a filter or interpreting pagination tokens.

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 'Search model versions' and provides the API endpoint. It distinguishes the tool from sibling tools like get, create, delete by focusing on searching. However, it does not elaborate on the scope of search (e.g., across all experiments or models).

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 is provided on when to use this tool versus alternatives like `mlflow_model_versions_get` for a single version. There is no mention of prerequisites, filter syntax, or pagination usage.

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