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

With no annotations, the description must disclose behavior. It fails to clarify whether the inference is synchronous or asynchronous, whether it returns a direct answer or a job ID (given the existence of animica_ai_job_status), or what side effects (if any) occur. The phrase 'OpenAI-compatible inference' hints at a direct API call but doesn't confirm response behavior.

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 very short and front-loaded, but 'Cheap general' is vague and adds little value. It is not bloated, but it is under-specified, which prevents it from being considered highly concise in a useful way.

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?

Given the complexity of AI inference and the presence of related tools like animica_ai_job_status, the description is incomplete. It doesn't explain the expected output, whether a job is created, or how the response is delivered. The output schema exists but is not referenced in the description, leaving the agent to guess at the tool's full behavior.

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 schema has 0% description coverage, so the description must compensate. It does not explain any of the five parameters (model, prompt, system, max_tokens, temperature). While the parameter names are standard for AI inference, the description adds no semantic meaning beyond the schema itself.

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 states the tool's function: 'Ask Animica's ENA AI a question' with the qualifier 'OpenAI-compatible inference'. This identifies the resource (ENA AI) and the action (ask). It is distinct from sibling tools like animica_ai_job_status or animica_ai_models, which focus on other aspects. However, it lacks specifics on the scope of 'question' or output format, so it's not 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 offers only 'Cheap general' as guidance, implying a low-cost, general-purpose use but provides no explicit when-to-use or when-not-to-use context. There is no mention of alternatives, prerequisites, or situations where another tool (e.g., animica_ai_job_status) would be more appropriate. This leaves the agent without clear selection criteria.

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