local_status

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

Annotations already declare readOnlyHint=true and openWorldHint=false, so safety and locality are covered. The description adds the useful behavioral framing that it is a diagnostic/precondition probe and a failure-triage entry point. It does not mention cost, latency, or caching, so it stops short of full disclosure.

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?

Two short sentences and a bulleted intent list, front-loaded with the one-line purpose before the usage cases. Nothing is padded and every clause carries information.

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 zero-parameter, read-only probe with annotations covering safety and an output schema covering the return shape, the description supplies everything an agent needs: what it inspects, when to call it, and that it serves as a diagnostic for the sibling local_* tools.

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 tool takes zero parameters, so per the rubric the baseline is 4; there is no parameter meaning for the description to add or omit.

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?

States a specific verb+resource+scope: 'Read-only diagnostics of the local backend and the model catalog.' This clearly separates it from the sibling local_* action tools (classify, summarize, delegate, etc.), which perform work rather than report state.

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?

Gives three concrete when-to-use conditions: discover which local models exist, verify the backend is alive before bulk delegation, and triage a failed local_* tool. It effectively routes the agent to this tool as the precondition check for the other local_* siblings.

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