ai_services

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

With no annotations, the description carries more burden. It usefully discloses that API keys are never accepted here and that default=true is consumed by backend='auto', but it omits side effects, persistence, permissions, and what a no-service call returns.

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?

The description is compact and front-loads the core resource and operations. The parenthetical service/task lists are dense but functional, and the API key caveat 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 configuration tool with no annotations, no output schema, and 0% schema description coverage, the description is only moderately complete. It covers parameters and some auth behavior but leaves return shape, persistence, and side effects unspecified.

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%, so the description must compensate. It names all four parameters in context, gives the nested models shape as {task: model}, explains default semantics, and lists candidate service names, adding substantial meaning beyond the bare schema.

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 clearly identifies the resource as online AI services and explains the two modes: listing readiness/tasks/models, and setting a service's models, url, or default. It does not explicitly distinguish itself from sibling tools like ai_status or generate_image, so it falls short of a 5.

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 description explains how to use the service parameter (set models/url/default) but gives no guidance on when to call this tool versus siblings such as ai_status or generate_image. There are no exclusions or alternative-routing instructions.

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