suggest_models

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

With no annotations provided, the description carries full burden for behavioral disclosure. It mentions the tool 'suggests' models but doesn't describe what the suggestion looks like (e.g., ranked list, single recommendation), whether it requires model availability checks, or any performance characteristics. The description is minimal and doesn't provide adequate behavioral context for a tool with no annotation coverage.

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, efficient sentence that front-loads the core purpose. Every word earns its place: 'Suggests' (action), 'best locally installed model' (resource and scope), 'for a specific task' (context), 'based on user needs' (input basis). No wasted words or redundancy.

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 tool with no annotations and no output schema, the description is insufficiently complete. It doesn't explain what the suggestion output looks like (e.g., model names, scores, explanations), doesn't mention potential errors (e.g., no models installed), and provides minimal behavioral context. Given the complexity of model selection and lack of structured output documentation, the description should do more.

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 100%, so the schema already fully documents both parameters. The description adds no additional parameter semantics beyond what's in the schema - it mentions 'user needs' and 'priority' but provides no extra context about format, examples, or constraints. Baseline 3 is appropriate when schema does all the work.

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 tool's purpose: 'Suggests the best locally installed model for a specific task based on user needs.' It specifies the verb (suggests), resource (locally installed model), and scope (for a specific task). However, it doesn't explicitly differentiate from siblings like 'select_chat_model' or 'list_local_models' beyond the 'suggests' vs 'selects' or 'lists' distinction.

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 usage context ('for a specific task based on user needs') and scope ('locally installed'), but doesn't provide explicit guidance on when to use this tool versus alternatives like 'select_chat_model' or 'list_local_models'. It mentions the 'priority' parameter which hints at trade-offs, but lacks clear when/when-not instructions or named alternatives.

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