suggest_model

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

The description discloses ranking behavior: by task match then memory fit, and mentions detection of free GPU VRAM or system RAM. Without annotations, this provides good transparency, though it could specify whether multiple GPUs are considered or what 'fit' means precisely.

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?

Two sentences, directly states purpose and output. No wasted words. Front-loaded with the core verb 'Recommend'.

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 simplicity of one parameter and no output schema, the description covers all essential aspects: input (task), ranking criteria (task match, memory fit), and output (ranked list with fit flags). Leaves no major gaps.

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 single parameter 'task' is fully defined by the enum in the schema. The description adds context that the task is used for ranking models, which is a slight enhancement beyond the schema's label.

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 recommends local models for specific tasks, ranks by task match and memory fit, and returns a ranked list with fit flags. It distinguishes itself from sibling tools like list_models and fit_check by combining recommendation and system resource awareness.

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 using this tool for model recommendations but does not explicitly state when to prefer it over alternatives like list_models, fit_check, or search_available. No guidance on prerequisites or context.

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