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

With no annotations, the description carries the full burden and does disclose the meaningful behavioral trait: a three-state status machine with defined meanings for each state. It omits whether api_key is required for access and any latency/cost behavior, which keeps it short of a 5.

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?

Three short sentences, front-loaded with the core purpose and followed by the return states and usage context. No filler.

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?

An output schema exists, so the return format need not be explained, yet the description helpfully enumerates states anyway. For a two-param health-check tool this is nearly complete, with the only gap being the role of the api_key parameter.

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?

Schema description coverage is 0% for both parameters. The phrase "models on disk" loosely gestures at model_dir, but the description never explains model_dir or api_key semantics, defaults, or format, so it does not compensate for the coverage 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?

States a specific verb+resource (liveness/readiness check plus cache occupancy) and enumerates the exact return states (ready, warm_required, no_models). This distinguishes it from sibling covatox_warmup, which the warm_required state implicitly points to.

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?

"Use as K8s liveness/readiness probe" gives a clear operational context for calling it. It does not explicitly name covatox_warmup as the follow-up when warm_required is returned, leaving that inference to the agent.

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