Test LM Studio structured output

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

With no annotations provided, the description must carry the full burden. It discloses the core behavior (testing enforcement and validating with Ajv) but omits operational details such as prerequisites (e.g., running LM Studio), whether it is read-only, or side effects. The mention of Ajv validation adds some transparency, but important behavioral context is missing.

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, concise sentence with no fluff, front-loading the purpose and the validation method. Every word earns its place. It is appropriately sized for the information it conveys.

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?

The tool has two parameters, no annotations, and an output schema, but the description fails to explain the parameters or provide operational context. While the output schema exists and need not be described, the lack of parameter semantics and prerequisites makes the description incomplete for reliable invocation. An agent would struggle to correctly choose values for 'model' and 'endpoint'.

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 has 0% description coverage, and the description does not mention the 'model' or 'endpoint' parameters at all. It fails to explain how these parameters affect the test, leaving the agent to guess their meaning and usage. This is a complete lack of compensation for the schema gap.

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 'Test' and names the resource 'JSON Schema enforcement', further clarifying it tests this independently from JSON mode and prompt-only JSON. It clearly distinguishes from sibling tools like lmstudio_test_inference and lmstudio_test_endpoints.

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 provides context for when to use this tool: to test JSON Schema enforcement specifically, excluding JSON mode and prompt-only JSON. However, it does not explicitly name alternative tools or give exclusionary guidance like 'use lmstudio_test_inference for inference tests'.

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