recommend_model

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

No annotations provided, so description carries full burden. Mentions return content (prioritized list, installation status, license info) but does not explicitly state side effects or whether it modifies system state. Assumed read-only.

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, front-loaded with action and output. Every sentence adds value. No filler.

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 no output schema or annotations, description is fairly complete: specifies inputs (implicit via system specs), return structure (prioritized list, status, license). Could clarify that it does not perform downloads.

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%, both parameters have descriptions. The description adds no additional meaning beyond what is in the schema (category enum and prefer_quality boolean). Baseline 3 applies.

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?

Clearly states it recommends the best local LLM model for a given task category based on system specs and installed models, returning a prioritized list. Distinguishes from sibling tools like list_loaded_models or pull_model.

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?

Implies usage for model selection but does not explicitly state when to use it versus alternatives like configure_model_selector or list_loaded_models. No when-not guidance.

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