Inspect an LM Studio model

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

Since no annotations are provided, the description carries full responsibility. It discloses that it returns 'discoverable' metadata and distinguishes static from runtime configuration, but it does not mention whether it is read-only, what happens for unknown models, or any side effects. This leaves gaps in behavioral expectations.

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 that front-loads the action and outcome, with no filler words. It is concise and structured clearly.

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 existence of an output schema and a single well-defined parameter, the description is sufficient for an agent to understand what the tool does and how to invoke it. However, it could mention prerequisites (e.g., that the model must exist locally) but this is minor.

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 schema provides 100% coverage with a clear description of the 'model' parameter as 'Model id, key, path, or identifier.' The tool description does not add further parameter details, but the baseline of 3 applies because schema coverage is high and the parameter meaning is clear.

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 uses a specific verb ('Return') and identifies the resource ('all discoverable local metadata') for a specific model. It also distinguishes two aspects (static capability vs runtime configuration), setting it apart from sibling tools like lmstudio_list_models which list all models.

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 usage for a single model's metadata, but it does not explicitly mention when to prefer this over alternatives such as lmstudio_list_models or lmstudio_capabilities. There are no explicit exclusions or 'use X instead' guidance, so the usage context 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.