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 non-redundant behavior: 'Keys are never shown,' telling the agent that credentials are withheld from the result. It also specifies what the response covers (per-job model assignments), which is useful given there is no output schema.

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 dense sentence with the scope front-loaded and no filler. The parenthetical provider list and job-type glosses are relevant rather than padding. It is efficiently sized for a parameterless status tool.

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 covering the safety profile, the description carries the remaining burden and mostly does so — it enumerates the providers reported, the job categories assigned models, and the key-withholding guarantee. The only gap is the absence of a pointer to set_ai_models for making changes, which would complete the picture.

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 clarify and the schema is trivially complete. Baseline 4 applies. No parameter semantics are needed or missing.

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 states a specific resource and scope: which AI providers are connected and which model each job type (text, vision, image, transcribe) uses. It goes beyond the title by enumerating the job categories examined. It does not, however, distinguish itself from the sibling write counterpart set_ai_models, leaving the read-vs-configure split implicit.

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?

Usage is only implied — the read-only, informational nature suggests 'call this to inspect configuration.' There is no explicit when-to-use statement, no exclusion, and no pointer to set_ai_models as the alternative when the agent wants to change providers or models. An agent can infer the purpose but must guess at the routing decision.

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