get_model_suggestions

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

With no annotations, the description fully covers behavioral aspects: auto-detection of client environment, filtering logic for native vs. aggregator clients, and optional provider highlighting. No destructive actions or rate limits are mentioned, which is appropriate for a read-only suggestion tool.

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?

Three concise sentences: purpose, auto-detection behavior, and filtering detail. No redundant information. Front-loaded with the main action.

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 the simple input schema and no output schema, the description adequately covers the tool's behavior and parameter usage. It could mention the output format (e.g., list of model names) but the current level is sufficient for selection and invocation.

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 100% with enum descriptions. The description adds value beyond the schema by explaining that preferred_provider is auto-detected if omitted and that tier affects the suggestion logic. This supplements the schema effectively.

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 'Returns suggested models for a tier', which is a specific verb and resource. It further explains auto-detection of client environment and filtering logic, differentiating it from sibling tools like classify_task and format_plan_block.

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 provides clear context: native clients receive only their provider's models, aggregator clients receive top 4 across providers. It also advises using preferred_provider to highlight a specific provider. It lacks explicit when-not-to-use instructions but is generally helpful.

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