list_models

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

No annotations are provided, so the description carries the burden. It explains that the tool queries an API and returns model data, but it does not disclose whether the operation is read-only, any error conditions, or permission requirements. This is adequate but not thorough.

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 concise: three sentences and three examples. The purpose is front-loaded, and every sentence adds value. No wasted words.

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 simple tool with one optional parameter and an output schema, the description covers the return type and metadata fields. It lacks details on error handling, pagination, or performance implications, but it is largely complete for common use cases.

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 input schema defines only one parameter with an enum, and the description adds value by explaining its purpose (filtering by model type) and providing examples. Since schema description coverage is 0%, the description compensates well for the only parameter.

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's purpose: 'List models available in the local LM Studio instance.' It specifies the API endpoint and the rich metadata returned. However, it does not explicitly distinguish itself from sibling tools like 'get_loaded_models', which could cause confusion.

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 guidance is provided on when to use this tool versus alternatives such as 'get_loaded_models' or 'get_model'. The examples show usage patterns but do not indicate when not to use the tool or which scenarios each sibling is suited for.

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