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

Annotations already mark this as non-read-only, non-idempotent, and non-destructive. The description adds valuable behavioral context: tokens are billed to the project's credit pool, and the response follows the OpenAI-compatible chat-completions format. This goes beyond the structured annotations and helps an agent anticipate side effects and output shape.

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?

Two crisp sentences deliver the core action, the output format, the billing consequence, and the sibling discovery route. There is no filler or redundancy; every clause carries useful information.

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 tool with no output schema, the description compensates by naming the response format. It also covers billing and model discovery. Remaining gaps are minor, such as the default model behavior when 'model' is omitted and whether prior authentication is required, but an agent has enough to invoke the tool effectively.

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 100%, so the baseline applies and the description need not repeat parameter details. The description does reinforce the model parameter's dependency on scalix_ai_models and the OpenAI-compatible response format, but it does not add substantial new meaning to individual parameters beyond the 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 opens with a specific verb and object: 'Run AI inference on Scalix AI.' It clearly distinguishes this tool from its sibling scalix_ai_models by stating it sends a prompt and returns the model's response, while pointing to the sibling for model discovery.

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 provides clear usage context: it is the inference tool, and it explicitly directs the agent to scalix_ai_models to discover available model IDs. It does not explicitly list exclusions or alternative tools for comparable tasks, but among the siblings this is sufficiently differentiated.

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