get_ml_model_by_name

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

No annotations are provided, so the description must convey behavioral traits. 'Get details' implies a read operation, but it does not explicitly state that the tool is read-only, has no side effects, or requires any permissions. The lack of behavioral context is a gap.

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 a single, concise sentence with no extraneous information. It is appropriately sized for a simple retrieval tool.

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 no annotations or output schema, the description should provide sufficient context for correct use. It only states the basic purpose and one parameter, omitting details about the response contents, optional fields parameter usage, and any constraints (e.g., case sensitivity, existence guarantees). This is insufficient for a tool within a large set of sibling operations.

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 two parameters (fqn, fields) with 0% description coverage. The description explains that 'fqn' is a fully qualified name, which adds value. However, it does not explain the 'fields' parameter (selective retrieval of specific fields). This incomplete guidance leaves the agent underinformed.

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 'Get details of a specific ML model by fully qualified name', specifying the action (get details), resource (ML model), and identifier method (fully qualified name). This distinguishes it from siblings like 'get_ml_model' (likely by ID) and 'list_ml_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?

The description implies using this tool to retrieve a model by its fully qualified name, but it provides no explicit guidance on when to use it versus alternatives such as 'get_ml_model' (by ID) or search tools. No when-not-to-use or alternative tool names are mentioned.

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