knowledge_ask

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and openWorldHint=true. The description adds valuable behavioral context: it uses 'Weaviate AsyncQueryAgent in ask mode' and produces 'source citations.' This goes beyond annotations by revealing the internal mechanism and output style.

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 concise sentences. The first sentence is a clear, front-loaded purpose statement. The second provides additional context (how it works, source citations) without waste. Every word earns its place.

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 annotations and output schema (present), the description adequately covers the tool's behavior. It explains the result (AI-generated answer with citations) but does not detail response format beyond that. However, the output schema likely covers structure, so this is sufficient.

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% with descriptions for both parameters. The description adds no new meaning beyond what the schema provides; it reinforces the role of 'query' as a question and 'collections' as scope. Baseline 3 is appropriate.

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: 'Ask a question and get an AI-generated answer grounded in stored knowledge.' The verb 'ask' and resource 'knowledge' are specific, and the description distinguishes this from sibling tools like knowledge_search (raw results) and knowledge_query (likely structured queries) by emphasizing synthesis and citations.

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 implies usage for natural language Q&A with synthesized answers, but it does not explicitly state when to use this tool over alternatives. The phrase 'synthesize an answer from objects across knowledge collections, with source citations' hints at differentiation from raw search, but no explicit 'when not' or alternative names are given.

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