AI providers and models

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

Annotations already declare readOnlyHint=true and openWorldHint=false, so the safety profile is covered. The description adds genuinely new behavioral context: the enumerated provider set returned, the four job categories each model is assigned to, and the privacy guarantee that keys are never shown — which matters because an agent might otherwise ask for credentials. It stops short of describing the response shape per provider.

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 sentence with the resource and scope front-loaded, plus one short clause on key privacy. The provider enumeration is long but earns its place by setting expectations for the return value. Nothing is redundant.

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?

With no parameters, no output schema, and annotations already covering the read-only nature, the remaining burden is describing what the call reveals. The description covers providers, per-job model assignment, and the key-redaction guarantee, which is sufficient for an agent to call it and interpret the result, though it omits any hint about the response structure or how unconfigured jobs are reported.

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; the baseline for a parameterless tool applies. The description correctly does not fabricate options and instead spends its words on return content.

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 uses a concrete interrogative framing — reporting which AI providers are connected and which model each job (text, vision, image, transcribe) uses — and enumerates the providers, so the agent knows exactly what comes back. It does not name the sibling it complements, set_ai_models, so the read-vs-configure distinction is only implied by the word 'status'.

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?

There is no explicit when-to-use sentence, no prerequisites, and no pointer to set_ai_models as the tool for changing the configuration. Usage is only implied: an agent can infer this is the read-only configuration inventory, but it must make that inference itself.

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