ask

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

Without annotations, the description carries the full burden of behavioral disclosure. It mentions 'automatic Unity detection, dynamic token scaling, and response headers with backend tracking', which are useful behavioral traits. However, it does not cover error handling, rate limits, or what happens during fallback chains, leaving gaps.

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 concise, fitting in a single sentence that lists key features. It is front-loaded with the main purpose. While it could be better organized (e.g., separating usage from features), it avoids unnecessary verbosity.

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 tool's complexity (8 parameters, no output schema), the description is insufficiently complete. It lacks details on the response format, error scenarios, and the behavior of the smart fallback chains. The agent would benefit from more context to use 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 is 3. The description adds minimal extra meaning beyond the parameter descriptions in the input schema. It provides a high-level context of 'smart fallback chains' but doesn't elaborate on individual parameters beyond what the schema already provides.

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 purpose as a multi-AI direct query tool with smart fallback chains. It specifies the action ('ask') and the resource ('any backend'). However, it lacks explicit differentiation from sibling tools like 'council' or 'explore', keeping it from a perfect score.

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 does not provide any guidance on when to use this tool versus alternatives. It omits context such as prerequisites, exclusions, or typical use cases, making it insufficient for an agent to decide when to invoke this tool over others.

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