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

No annotations are provided, so the description carries the full behavioral burden. It usefully discloses what is inspected and reported (backend type, model name, key validation), but is silent on whether the check performs a live network/API call, possible latency or failure modes, and how an unavailable result should be interpreted.

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 sentences, zero waste: the first front-loads what is checked and returned, the second front-loads the calling guideline. Every clause earns its place.

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, no-argument health-check with no output schema, the description compensates by naming the three returned fields and the availability signal. It stops short of describing error/unavailable states or how to act on them, which is the only remaining gap.

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 (schema coverage 100%, empty object), so there is no parameter semantics to explain; the baseline of 4 applies. The description correctly adds no redundant parameter talk.

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 and resource: checking availability of the refactoring service and the DeepSeek cloud model, plus the returned fields (backend type, model name, key validation status). It is unmistakably distinct from the sibling refactor_code, which performs the actual refactoring rather than checking readiness.

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?

Explicitly routes the agent: 'suggest calling this tool before calling refactor_code.' The sibling is named and the condition that selects this tool (pre-flight precondition) is stated, leaving nothing to inference for a zero-parameter status check.

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