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Write persistent knowledge from conversations by specifying the type (lesson, decision, or playbook) and content. Automatically routes to the correct storage format.

Instructions

统一知识写入入口 — 根据 kind 自动路由到 add_lesson / add_decision / add_playbook。 Unified knowledge write endpoint — routes to add_lesson / add_decision / add_playbook based on kind.

**Lifecycle: writeback** — 对话中产生值得长期保留的知识时调用。
Lifecycle: writeback — call when the conversation produces knowledge worth persisting.

这是 Provider 兼容的统一写入接口。如果你已经明确知道要写 lesson/decision/playbook,
也可以直接调用对应的专用工具。本工具的优势在于:调用方不需要知道 Engram 内部的分类体系。
This is a provider-compatible unified write interface. You may also call the specialized
tools directly. The advantage here: callers don't need to know Engram's internal taxonomy.

Args:
    kind: 知识类型 — 'lesson' | 'decision' | 'playbook'。 / Knowledge type.
    content_json: 知识内容 JSON 字符串。格式因 kind 而异:
        - lesson: {"summary": "...", "detail": "...", "domain": "..."}
        - decision: {"question": "...", "choice": "...", "reasoning": "..."}
        - playbook: {"title": "...", "triggers": "...", "steps_json": "[...]"}
        Content JSON string. Schema varies by kind (see above).
    source_tool: 调用来源工具(可选),如 'claude_code', 'cursor'。 / Source tool (optional).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
kindYes
content_jsonYes
source_toolNo

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 description bears full burden. It explains writeback lifecycle and routing behavior, but lacks detail on potential side effects or limitations.

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?

Bilingual description is structured and front-loaded with purpose, but longer than necessary; still efficient for complexity.

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?

Covers routing, parameter details, lifecycle, and alternatives. Output schema exists, so return values need not be explained.

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 0%, but description fully documents each parameter: kind enum values, content_json formats per kind, and source_tool optionality, adding significant meaning.

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 it's a unified knowledge write endpoint that routes to add_lesson/add_decision/add_playbook based on kind, distinguishing it from sibling specialized tools.

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 call (conversation produces knowledge worth persisting) and when to use alternatives (directly call specialized tools if kind is known).

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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