ask_ai

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

With no annotations provided, the description discloses key behaviors: RAG over PUBLISHED documents (excluding drafts), returns synthesized answer plus source IDs, and is stateless with no memory of prior calls. It adds meaningful context beyond the schema, though it does not mention error handling or authentication requirements.

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 two focused sentences, front-loaded with the core action and including critical distinctions (published docs, one-shot, source IDs). Every sentence earns its place with no filler.

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 no output schema, the description sufficiently explains the return content (synthesized answer + source documents with documentIds) and the tool's scope (published documents, one-shot). It could mention edge cases like zero sources or filter behavior, but the description is complete enough for a typical QA use case.

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 coverage is 100% and both parameters (question and categoryIds) are already well described in the input schema. The tool description adds little extra semantic value beyond what the schema provides, but the schema itself is sufficient.

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: asking a natural-language question to the Meet Rupert AI via RAG, returning a synthesized answer with source documents. It distinguishes itself from siblings by explicitly contrasting with ask_in_thread for multi-turn conversation.

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 explicitly says when to use this tool (one-shot question) and provides a direct alternative: 'use create_chat_thread + ask_in_thread for a multi-turn conversation.' This gives the agent clear decision criteria, and it even hints at follow-up via read_document.

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