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wrap_up_session

End a conversation by extracting lessons, decisions, and playbook drafts from the summary. Optionally save or update a project snapshot with folder, title, and tech stack details.

Instructions

会话结束一键收尾:自动提取知识、操作流程并保存项目快照。 / Wrap up a session in one step: extract knowledge, detect playbooks, and save a project snapshot.

**Lifecycle: session-end** — 对话结束时调用,完成知识提取和上下文保存。
Lifecycle: session-end — call at conversation end to extract knowledge and persist session context.

用途:一次对话结束时调用,把会话摘要交给 Engram 自动提取 lessons、decisions 和 Playbook 草稿,并可选更新项目快照。
Purpose: Call at the end of a conversation to let Engram extract lessons, decisions, and playbook drafts from the summary and optionally update the project snapshot.

Playbook 自动提取:如果摘要描述了一个多步骤操作流程(3+ 步骤,含顺序标记和操作动词),会自动生成 Playbook 草稿存入 staging。返回值中会包含 playbook_draft 字段(含 confidence: high/medium),AI 工具应根据 confidence 决定是否提示用户。可通过 update_preferences(playbook_auto_extract=false) 关闭此功能。
Playbook auto-extraction: If the summary describes a multi-step operational workflow (3+ steps with sequential markers and action verbs), a Playbook draft is auto-generated into staging. The return value includes a playbook_draft field (with confidence: high/medium); AI tools should decide whether to notify the user based on confidence. Disable via update_preferences(playbook_auto_extract=false).

注意:如果只想提取知识不用保存项目,用 extract_session_insights;如果只想保存项目快照,用 save_project_snapshot。
Note: Use extract_session_insights when you only want extraction, and save_project_snapshot when you only want to save a project snapshot.

Args:
    summary: 会话摘要(自由文本,段落或要点列表均可)。 / Session summary in free text; paragraphs or bullet lists both work.
    project_folder: 项目文件夹路径(可选,不填则只提取知识不保存快照)。 / Project folder path (optional; omit it to extract knowledge without saving a snapshot).
    source_tool: 调用来源工具,如 'claude_code', 'codex'。 / Calling source tool, such as 'claude_code' or 'codex'.
    project_title: 项目名称(可选,仅在首次保存快照时需要)。 / Project title (optional; mainly needed when first saving a snapshot).
    tech_stack: 技术栈(可选,逗号分隔)。 / Tech stack (optional, comma-separated).
    known_issues: 已知问题(可选,逗号分隔)。 / Known issues (optional, comma-separated).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
summaryYes
tech_stackNo
source_toolNo
known_issuesNo
project_titleNo
run_reconcileNo
project_folderNo
user_confirmedNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior5/5

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

With no annotations provided, the description fully assumes the behavioral disclosure burden. It details the auto-extraction of playbooks (with confidence levels and disable option), the conditional snapshot saving, and the return value structure including playbook_draft. All behavioral aspects are transparent.

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 comprehensive but somewhat lengthy, using bilingual text and multiple sections. Key information is front-loaded, and the structure is good, but could be slightly more concise without losing value.

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?

Given the tool's complexity (8 parameters, side effects, output fields), the description covers purpose, usage guidelines, parameter semantics, behavioral nuances (auto-extraction, optional snapshot), and return value details. No output schema is needed for completeness as the description provides adequate context.

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?

Despite 0% schema description coverage, the description provides detailed explanations for each parameter in the Args section, including optional parameters and their effects (e.g., project_folder omitted means no snapshot, project_title needed only for first snapshot). This fully compensates for the schema gap.

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's purpose: 'Wrap up a session in one step: extract knowledge, detect playbooks, and save a project snapshot.' It explicitly contrasts with sibling tools extract_session_insights and save_project_snapshot, providing clear differentiation.

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?

The description gives explicit lifecycle guidance ('Lifecycle: session-end — call at conversation end') and directly states when to use this tool vs alternatives ('Use extract_session_insights when you only want extraction, and save_project_snapshot when you only want to save a project snapshot.').

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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