Ask AI engines

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

Annotations already declare readOnlyHint and openWorldHint, so the external-call, non-mutating nature is covered. The description adds genuinely useful output context (raw answers plus cited sources) and fan-out behavior across engines, but it omits cost, latency, and failure behavior for a call that hits four external providers. There's also mild tension between 'every configured AI engine' and the optional engines parameter that lets you pick a subset.

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 sentences, front-loaded with the core action, with the example inlined rather than relegated to a separate clause. Every sentence carries information and nothing is padding.

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 two-parameter read tool with no output schema, the description supplies what's needed to call it: the input concept, an example, fan-out scope, and the shape of the return. The gaps are the engines parameter behavior and operational traits (cost, latency) of multi-provider fan-out.

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 bears the burden of explaining both parameters. It does well on question by describing it as one customer-style question with a realistic example, but the engines parameter is never explained — only vaguely implied by 'every configured AI engine', leaving the subset/selection behavior undocumented.

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 names a specific verb and resource ('Ask one customer-style question ... to every configured AI engine') and states the return ('each raw answer plus any cited sources'), with a concrete example question that pins down the expected input shape. It doesn't name the sibling tools (check_visibility, suggest_questions, compare_competitors), so the distinction has to be inferred, which keeps it from 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?

'Use this to read exactly what an assistant tells customers' gives a clear motivating scenario for the tool, so the intent is implied rather than left empty. However, it offers no when-not guidance and never mentions when an agent should prefer check_visibility, compare_competitors, or suggest_questions instead.

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