get_model

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

With no annotations, the description carries the burden. It communicates that the call returns data rather than performing an update, which is useful, but it does not state whether the operation is read-only, whether the measurement summary is stored or recomputed, or what happens for unknown names.

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, front-loaded with the verb and output, and no filler. Every word adds information.

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 one-parameter getter this is mostly sufficient, and no output schema exists so describing the return shape at a high level helps. It is incomplete as a standalone spec because it omits valid-name sourcing, potential errors, and explicit differentiation from sibling tools.

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 coverage is 0%, so the description must compensate. 'a model's stored spec' indicates that 'name' identifies a model, but it does not explain the naming convention or mention using list_models to find valid names. The single obvious 'name' parameter makes the gap minor.

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 names a specific verb ('Return') and resource ('a model's stored spec'), and identifies the output format (JSON) plus a measurement summary. It is clear for a getter, though it does not explicitly contrast with sibling inspection tools like 'look' or 'check'.

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 given for when to call get_model instead of list_models, look, check, or measure. There are no prerequisites or exclusions, so an agent must infer the intended use from the name alone.

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