get_model

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

No annotations are provided, so the description carries the full burden for behavioral disclosure. It only says 'Get details and capabilities', which essentially restates the tool name without elaborating on read-only nature, required permissions, error behavior, or what specific capabilities are returned.

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 clear, front-loaded sentence with no redundant information. It efficiently communicates the tool's purpose without wasting words.

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?

With only one parameter and no output schema, the description is minimal but adequate. However, it lacks specificity about the exact details and capabilities returned, and provides no usage context or prerequisites, which could leave an agent uncertain about whether this tool meets its needs.

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 100% of parameters (modelId with description 'ID of the model to retrieve'). The description adds no additional parameter meaning beyond the schema, so the baseline score of 3 applies.

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 the verb 'Get' and resource 'details and capabilities of a specific AI model', which distinguishes it from sibling tools like list_models (which lists all models) and switch_model (which changes the active model).

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 this tool is for retrieving a specific model by ID, but provides no explicit guidance on when to use it versus alternatives, nor mentions situations where it should not be used. The distinction from list_models is inferable but not stated.

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