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capture_answer

Preserve substantive Q&A answers as knowledge cards. Use when a conversation provides technical insight or actionable knowledge not found in standard references.

Instructions

Capture a useful Q&A answer as a draft knowledge card.

Use this ONLY when a conversation produces a SUBSTANTIVE answer that is worth persisting — meaning it provides genuine technical insight, a non-obvious explanation, or actionable knowledge that cannot be found in standard references.

DO NOT use this tool for:

  • Single-sentence answers or brief definitions

  • Answers that could be found in any standard reference (Wikipedia, docs)

  • Trivial facts, simple yes/no responses, or content shorter than 150 characters

  • Paper or topic-level knowledge that requires source verification

If you have structured evidence and claims, prefer save_research instead — it produces higher-quality cards with proper source attribution.

The answer text MUST be at least 150 characters. Write a thorough explanation covering the key insight, context, and practical implications.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
tagsNoComma-separated tags for the card (optional).
queryYesThe question that was answered.
answerYesThe answer text (plain text or markdown). Minimum 150 characters.
languageNoLanguage for the card content — "zh" (Chinese, default) or "en" (English).zh

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior4/5

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

No annotations provided, so the description carries the full burden. It discloses the 150-character minimum, content expectations, and the type of answer suitable. However, it does not mention any side effects or authorization requirements, though the tool is likely non-destructive. Overall, good transparency.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is front-loaded with purpose and then provides clear usage guidelines in a bullet-like structure. While slightly verbose, every sentence adds value, and the structure is logical and easy to parse.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With output schema present and full schema coverage, the description covers purpose, usage guidelines, parameter semantics, and context relative to siblings. It is complete enough for an AI agent to correctly select and invoke the tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, but the description adds significant value: clarifies tags as optional, language defaults, and importantly the 150-character minimum for answer. It also explains the expected content quality, which goes beyond the schema's description.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool captures a Q&A answer as a draft knowledge card, specifying it is for substantive answers from conversations. The verb 'capture' and resource are distinct and well-defined, and it differentiates from sibling tools like 'save_research'.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly states when to use (substantive answers worth persisting) and when not to use (single-sentence, standard references, etc.), and provides a clear alternative: 'save_research' for structured evidence. This gives excellent guidance to an AI agent.

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

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