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protools_document_suggest

Generate structured documentation from code or config changes by analyzing git diffs or file inputs. Extract implicit specifications, design decisions, or changelogs with customizable doc types and output formats.

Instructions

从代码/配置变更中提取隐含规范,生成结构化文档。

文档类型

  • spec: 技术规范(配置格式、字段定义、约束规则)

  • decision: 设计决策(为什么这么做、权衡考量)

  • changelog: 变更日志(按类别分组的变更记录)

  • auto: 自动推断最合适的类型

输出格式

  • markdown: 标准 Markdown

  • feishu: 飞书优化格式(默认,避免 HTML、标题不超 3 级)

使用示例

  • 从 Git 变更生成技术规范:git_mode="staged", doc_type="spec"

  • 从文件生成设计决策:inputs=["src/**/*.yml"], doc_type="decision"

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
cwdNo工作目录,多仓库工作区时指定项目路径
formatNo输出格式:markdown=标准 Markdown | feishu=飞书优化格式feishu
inputsNo要分析的文件/目录/glob 路径列表(与 git_mode 二选一)
contextNo附加的上下文说明,帮助 LLM 理解变更背景
doc_typeNo文档类型:spec=技术规范 | decision=设计决策 | changelog=变更日志 | auto=自动推断auto
excludesNo排除的 glob 模式
git_modeNoGit diff 模式:staged=已暂存 | unstaged=未暂存 | all=全部未提交
languageNo输出语言:zh=中文 | en=英文zh
providerNoLLM Provider,默认 gemini(格式化更好)
extensionsNo过滤扩展名,如 [".yml", ".yaml"]
Behavior2/5

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

No annotations are provided, so the description must fully disclose behavior. It mentions using LLM and providers, but does not discuss safety (e.g., whether it modifies files, requires specific permissions, or handles destructive actions). For a tool with 10 parameters, this is insufficient.

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 well-structured with sections for document types, output formats, and usage examples. It is not overly verbose; every section adds useful information.

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

Completeness4/5

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

Given the complexity (10 parameters, no output schema), the description covers core functionality, parameter options, and examples. It could mention the return format (likely a document string) but is otherwise complete.

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

Parameters4/5

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

Input schema has 100% description coverage, providing clear explanations for each parameter. The description adds value by grouping parameters and offering usage examples, helping understand which combinations are appropriate.

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 extracts implicit specifications from code/config changes to generate structured documentation. It lists specific document types (spec, decision, changelog, auto) and output formats (markdown, feishu), distinguishing from sibling tools like code review or merge files.

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

Usage Guidelines4/5

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

Provides guidance on when to use each doc_type and format, with examples like 'from Git staged changes generate spec'. However, it does not explicitly state when not to use this tool or compare directly to siblings.

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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