brain_ml_models

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

With no annotations provided, the description adds critical behavioral context by stating that presence on disk does not mean the model is loaded or active, preventing a common misinterpretation. It also makes clear this is a listing operation, but it does not describe output format or error behavior for misconfigured directories.

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 two short sentences with no fluff. The core action and object are front-loaded, and the caveat is presented as a distinct, clearly separated clarifying sentence.

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 zero-parameter, presumably read-only listing tool, the description sufficiently covers the main action and key caveat. Minor ambiguities about how directories are 'configured' and the exact return format remain, but the absence of an output schema and the simplicity of the operation make this acceptable.

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?

There are zero parameters, so the baseline score is 4. The description adds contextual meaning by referencing 'configured model directories', giving a hint about the implicit configuration without needing parameter-specific details.

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 uses a specific verb ('List') and a clear resource ('on-disk ML model artifact files across configured model directories'). This distinguishes it from sibling tools like brain_where_deploys, which focus on deployment locations, by explicitly naming the on-disk artifact scope.

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 use when an inventory of on-disk model files is needed, but does not explicitly state when to prefer this tool over alternatives or when not to use it. The caveat about loaded/active status is semantic rather than a usage guideline, and there are no references to sibling tools.

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