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protools_code_review

Analyze code for security, performance, quality, and maintainability issues using AI. Supports file paths or git diff modes with OpenAI and Gemini.

Instructions

使用 AI 对代码进行审查,支持安全性、性能、质量和可维护性分析。支持 OpenAI GPT-5.2 和 Google Gemini 3 Flash。

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
cwdNo工作目录,多仓库工作区时指定要审查的项目路径(如 /home/user/Work/xxxx)
modeNo代码压缩模式:full=完整代码 | compact=移除注释和import | skeleton=仅保留类/方法签名(适合全仓库审查)compact
focusNo审查关注领域:security | performance | quality | maintainability | allall
inputsNo要审查的文件/目录/glob 路径列表(与 git_mode 二选一)
outputNo输出方式:inline=直接返回 | file=写入文件inline
contextNo附加的审查上下文或特殊说明
excludesNo排除的 glob 模式,如 ["**/test/**", "**/*.test.ts"]
git_modeNoGit diff 模式:staged=已暂存 | unstaged=未暂存 | all=全部未提交
providerNoLLM Provider,默认从 LLM_PROVIDER 环境变量读取
extensionsNo过滤扩展名,如 [".ts", ".js"]
output_dirNo输出目录(output=file 时使用)
include_full_filesNoGit diff 模式下,是否同时包含变更文件的完整内容以提供更好的上下文
include_project_contextNo是否包含项目信息(package.json、目录结构等)以帮助模型理解项目背景
Behavior2/5

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

With no annotations provided, the description carries full burden for behavioral disclosure. It fails to mention side effects like file writing (output=file), potential destructive actions, rate limits, or required permissions. The description is too brief for the tool's complexity.

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?

Two sentences, no redundant information. The structure is front-loaded with capabilities, then supported models. Could be improved with bullet points but remains efficient and focused.

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

Completeness2/5

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

Given 13 parameters, no annotations, and no output schema, the description is incomplete. It omits return values, error handling, prerequisites, and important behavior like file output or git mode interactions. Significant gaps for a complex tool.

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

Parameters3/5

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

The input schema has 100% coverage with descriptions for all 13 parameters. The description adds no extra meaning beyond the schema, so it meets the baseline of 3. No deficit, but no added value.

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

Purpose4/5

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

The description clearly states the tool performs AI code review for security, performance, quality, and maintainability. It specifies supported providers (GPT-5.2, Gemini 3 Flash), distinguishing it from sibling tools like protools_code_review_start and protools_code_review_status, but does not explicitly differentiate itself.

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

Usage Guidelines3/5

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

The description implies usage through parameters like mode and focus, but does not explicitly state when to use this tool versus alternatives (start, status) or provide exclusions. Guidance is ambiguous.

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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