list_llm_models

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

Without annotations, the description adds behavioral context by specifying that there are exactly 6 models and they are runtime-switchable. This provides useful information beyond a simple listing, though it does not cover other potential behaviors like authentication or side effects.

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 sentence with no unnecessary words. It is front-loaded with the key action and resource, achieving maximum conciseness.

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 list tool with no parameters and no output schema, the description is largely sufficient. It mentions the number of models and their runtime-switchability, but could be more complete by detailing the return format or what 'configured' entails.

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?

There are zero parameters, so the schema coverage is 100%. Per baseline, a score of 4 is appropriate as the description does not need to add parameter information, and it correctly implies no input is needed.

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 action ('List') and the resource ('configured LLM models'), adding specificity with the parenthetical '(6 runtime-switchable models)'. This distinguishes it from siblings like get_llm_model_health or get_metrics_llm.

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?

No explicit when-to-use guidance is provided. The purpose is clear, but the description does not mention alternatives or when not to use this tool. Usage is implied rather than stated.

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