get_llm_recommendations

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

No annotations are provided, so the description carries full burden. It mentions BETA status and remote API requirement, but omits critical traits such as error behavior, latency, network dependency, output format, or what happens if no models are found.

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?

Single sentence with parenthetical note. Very concise with no filler, though could benefit from slightly more detail to be fully self-contained.

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 output schema and no annotations, the description is incomplete. It lacks information about return values, error conditions, rate limits, or how the remote API connection is used. Param coverage is high but overall completeness is low for agent selection.

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 both parameters (limit, use_case) described. Description adds no extra meaning beyond schema definitions, so 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?

Description explicitly states 'Recommends the best LLM models that can run locally on this machine.' It includes a clear verb (recommends) and resource (LLM models), and distinguishes from sibling tools like check_llm_compatibility or analyze_inference_config.

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?

Description gives context ('requires remote API connection') but no explicit guidance on when to use versus alternatives. It implies usage when seeking local model recommendations, but does not state when not to use or compare with siblings.

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