local_status

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

No annotations are provided, but the description adequately describes the behavioral traits: it is a read-only status check that returns model availability, loaded model, and context. It implies no side effects, which is appropriate for a simple status tool.

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 efficiently conveys the purpose and usage. No wasted words; front-loaded with the core function.

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 parameters and no output schema, the description provides enough context about what it returns (available models, loaded model, configured context). It is complete enough for a simple status check, though a native English speaker might need translation.

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 no parameters, so schema coverage is 100%. The description does not add parameter details, but none are needed. Baseline for 0 parameters is 4.

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 returns the state of LM Studio, listing available models, loaded model, and configured context. It includes a specific usage scenario (error or unexpected slowness), distinguishing it from siblings like local_digest, local_map, and local_ask.

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 explicitly says when to call the tool (in case of error or slowness). It does not mention when not to use it, but the context is clear given the sibling tools have different purposes.

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