openai_health_check

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

No annotations are supplied, so the description carries the full disclosure burden; it establishes that the tool inspects configuration and makes a connectivity check, implying a network call, but does not say whether it is read-only, whether it consumes quota/rate limits, or how it behaves on failure. The presence of an output schema covers the return-value side, which limits the gap.

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?

A single front-loaded sentence with no filler, hedging, or restatement of the tool name. Every word contributes to the scope definition.

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 diagnostic with an output schema, the description covers what is being checked and against what target. Only minor gaps remain: what "local configuration" encompasses (env vars, key presence) and whether the connectivity probe has side effects.

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 there is nothing for the description to disambiguate; baseline 4 applies. The description also does not invent parameters that do not exist.

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 ("Check") and two concrete resources (local configuration and connectivity to the OpenAI Platform API), which clearly separates it from the sibling tools that all perform model, file, or generation operations. It does not explicitly name a sibling for contrast, but the diagnostic scope is unambiguous.

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 diagnostic intent is implied — an agent can infer this is a pre-flight/troubleshooting call — but the description never says when to run it versus just calling openai_safe_api_request or openai_list_models, nor does it state prerequisites (e.g., that an API key must be configured locally).

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