原型 · 预览版:收录官方注册中心里的全部服务器,每 30 分钟同步一次。评价和账户功能将在后续阶段推出。
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Agent Graph Auditor

作者:GuardBee版本 0.3.1i★ –i在你的电脑上运行
注册名称io.github.GuardBee/agent-graph-auditor

安装

概览

作者提供的说明(英文原文)

Finds transitive excessive agency across LangGraph/CrewAI/AutoGen orchestration graphs

PopNx MCP 的三大支柱

这三项永远不会合并成一个分数:意见、测量和人气是不同的东西。

它能帮你做什么

我们还没有为它写通俗的介绍。下面是我们从注册中心读取到的事实。

  • 连接方式: 在你的电脑上运行
  • 传输方式: stdio
  • 分类: 其他

安装

选择你使用的 AI 应用,然后复制设置。

安装之前

  • 基础检查:1 项中通过 1 项
  • 在你的电脑上运行,拥有你的用户账户的权限(文件、网络、应用)。
  • 注册中心没有列出需要填写的密钥。

你需要

  • npm已安装 Node.js(用于 npx 命令)

你用的是哪个应用?

在上方选择你的应用。我们会记住它,下次按钮会显示“安装到 …”。

打开 Claude Desktop → Settings → Developer → Edit Config,把下面的内容粘贴到 claude_desktop_config.json

{
  "mcpServers": {
    "agent-graph-auditor": {
      "command": "npx",
      "args": [
        "-y",
        "@guardbee/mcp-agent-graph-auditor"
      ]
    }
  }
}

在终端中运行这条命令

claude mcp add agent-graph-auditor -- npx -y @guardbee/mcp-agent-graph-auditor

在 Cursor 中打开

或者自己复制设置

粘贴到项目中的 .cursor/mcp.json,或 ~/.cursor/mcp.json

{
  "mcpServers": {
    "agent-graph-auditor": {
      "command": "npx",
      "args": [
        "-y",
        "@guardbee/mcp-agent-graph-auditor"
      ]
    }
  }
}

浏览器会先询问是否打开应用,然后应用会显示设置并请你确认。密钥的值会留空,由你自己填写。

在 VS Code 中打开

或者自己复制设置

粘贴到项目中的 .vscode/mcp.json

{
  "servers": {
    "agent-graph-auditor": {
      "type": "stdio",
      "command": "npx",
      "args": [
        "-y",
        "@guardbee/mcp-agent-graph-auditor"
      ]
    }
  }
}

浏览器会先询问是否打开应用,然后应用会显示设置并请你确认。密钥的值会留空,由你自己填写。

这个服务器在你的电脑上运行,而 ChatGPT 只能连接互联网上的服务器。请选择其他应用。

你的选择通过 Cookie 保存在这台设备上,无需账户。

这些设置是根据作者在注册中心的条目生成的。如果 README 的写法不同,请以 README 为准。

它能做什么

它在你的电脑上运行;安装后,工具列表会显示在你的 AI 应用中。

基础检查i

我们检查的是任何人都能看到的信息;我们没有阅读代码。不适用的检查项不计入总数。 · 检查于 2026年11月7日

  • ✓ 通过

    作者与仓库一致

    注册名称已通过 GitHub 账户 GuardBee 验证,该账户拥有此仓库

  • … 尚未检查

    仍在维护

    我们还没有从 GitHub 读取这个仓库

  • … 尚未检查

    有许可证

    我们还没有从 GitHub 读取这个仓库

  • – 不适用

    端点有响应

    它在你的电脑上运行,没有互联网端点

  • – 不适用

    使用 OAuth 登录

    在你的电脑上运行,改用本地设置

  • … 尚未检查

    工具描述中没有隐藏指令

    它在你的电脑上运行;我们只能读取互联网端点的工具列表

与“其他”中的其他服务器比较i

  • 星标数多于此分类中其他 32568 个服务器的 0%
  • 发布到注册中心的时间比此分类中其他 32568 个服务器的 82% 更新
  • 基础检查结果等于或优于此分类中其他 32568 个服务器的 100%

与注册中心里这个分类中所有活跃的服务器比较。只有当发布者拥有该仓库时,星标数才计入。

常见问题

Agent Graph Auditor 安全吗?

它在 1 项适用的基础检查中通过了 1 项。我们没有阅读代码。在添加密钥或令牌之前,请先看看这个服务器要求哪些权限,并使用只具备所需权限的密钥。

它需要哪些密钥或代码?

注册中心没有列出必需的密钥,不过有些服务器会在首次使用时要求你登录。

它在我的电脑上运行还是在云端?

它在你的电脑上运行。你的 AI 应用会在需要时启动它,数据不会经过作者的服务器(除非它自己调用在线服务)。

它还在维护吗?

GitHub 仓库最后更新于–,最新版本(0.3.1)于7天前进入注册中心。

哪些应用可以使用它?

支持 MCP 的应用,例如 Claude Desktop、Claude Code、Cursor 和 VS Code。你的电脑上还需要有 Node.js。

有什么可以替代它?

同一分类中有:Spec Driven Development, Remote Desktop Commander, FlyDocs

替代选择

同一分类中其他作者的服务器,按星标数排序。

如何看卡片:★ GitHub 星标i三大支柱:评价 · 检查 · 使用情况i更新 = 最新版本发布到注册中心的时间i

Spec Driven Development

作者:formulahendry

MCP Server that facilitates spec-driven development workflows, not just Vibe Coding.

在你的电脑上运行★ 440MIT14个月前更新
安装

Remote Desktop Commander

作者:desktop-commander

Remote Desktop Commander: use files, terminal and processes on your own computers from any AI app.

在云端运行★ 211昨天更新
安装

FlyDocs

作者:flydocs.ai

Spec-driven development workflow: your agent works issues, sessions and the status lifecycle.

在你的电脑上运行今天更新
安装

作者的 README

打开 README

按原文从作者的仓库复制。PopNx MCP 没有审查或背书其内容。HTML、图片和脚本已被移除。链接会离开本站。

@guardbee/mcp-agent-graph-auditor

🇬🇧 English | 🇹🇷 Türkçe | 🇨🇳 中文 | 🇯🇵 日本語 | 🇰🇷 한국어 | 🇧🇷 Português | 🇪🇸 Español | 🇩🇪 Deutsch

An MCP (Model Context Protocol) server that builds a reachability graph across a multi-agent orchestration and finds transitive excessive agency — an agent that was never directly given a dangerous tool, but can still reach one through another agent it's allowed to delegate to.

mcp-server-auditor catches an MCP server whose own tool grants shell/eval/SQL access directly. ai-code-scanner catches a single agent's tool list containing execute_command. Both of those are one hop: agent → tool. Multi-agent frameworks add a second kind of edge — delegation, group chat, function-execution routing — and a "safe-looking" agent with only a web_search tool can become dangerous the moment it's allowed to hand work off to a coworker that holds a shell tool. That two-hop path is invisible to a scanner that only looks at one agent's own tool list at a time.

Telemetry is off by default. With GUARDBEE_TELEMETRY=1 it sends the server, tool, success, duration and short parameter values such as a file path; longer strings and fields like content, token or password are replaced with a placeholder. See @guardbee/mcp-telemetry.

Claude ──► agent-graph-auditor ──► Your multi-agent Python source

│ ├─ LangGraph (add_node/add_edge — structural, read directly from the graph API) ├─ CrewAI (Agent/tools/allow_delegation + Crew membership — heuristic) └─ AutoGen/ag2 (GroupChat membership, code_execution_config, register_function — heuristic)


How the graph is built

  1. Extract. Framework-specific regex extractors pull agent/tool nodes and relationship edges out of the source: has_tool (an agent's own toolset), delegates_to (CrewAI allow_delegation, LangGraph add_edge), group_member (AutoGen GroupChat co-membership — any member's output can be routed to any other by the manager), executes_via (AutoGen register_function's caller/executor split).
  2. Classify. Every tool node is checked against a name-based capability catalog (code execution, process execution, filesystem write, network, credentials access) — the same kind of heuristic ai-code-scanner's excessive-agency pattern uses, just applied to a graph of tools instead of one server's tool list.
  3. Reach. A breadth-first search runs from every agent node. A capability tool reached via a straight has_tool edge is a direct finding (still reported — useful even without a second agent in the picture). A capability tool reached only after crossing at least one delegates_to/group_member/executes_via edge is transitive — the finding this package exists for — and is always reported as critical, regardless of the tool's own base severity, because the agent holding it was never audited for it directly.

LangGraph is the most reliable target: add_node/add_edge calls are the orchestration graph in the source, no inference needed. CrewAI and AutoGen require inferring delegation from framework semantics (allow_delegation, GroupChat membership) rather than an explicit edge in the code, so treat those as heuristic — see Limitations below.


Features

  • 3 frameworks: LangGraph (structural), CrewAI (heuristic), AutoGen/ag2 (heuristic)
  • 7 capability rules across 5 categories (code-execution, process-execution, filesystem-write, network, credentials-access)
  • Distinguishes direct (single-agent) from transitive (delegation-crossing) excessive agency — the transitive case is what no single-agent scanner in this monorepo catches
  • Multi-hop aware — a 3+ agent delegation chain is found the same way a 2-agent one is
  • SARIF 2.1.0 output — CI/CD integration (GitHub Code Scanning, etc.)
  • Config file support via guardbee.yml
  • 31 unit tests, plus end-to-end verification against realistic fixtures for all three frameworks

Quick Start

Claude Desktop / MCP Client
{

"mcpServers": { "guardbee-agent-graph-auditor": { "command": "npx", "args": ["-y", "@guardbee/mcp-agent-graph-auditor"] } } }

CLI (CI/CD)
npx @guardbee/mcp-agent-graph-auditor scan ./agents --fail-on=high --format=sarif > results.sarif

MCP Tools

ToolDescription
scan_textScans a Python snippet
scan_fileScans a single .py file
scan_directoryRecursively scans a directory of .py files
list_patternsLists the dangerous-capability catalog by category

Example

researcher = Agent(role="Researcher", tools=[web_search_tool], allow_delegation=True)

ops = Agent(role="Ops Engineer", tools=[shell_tool], allow_delegation=False) writer = Agent(role="Writer", tools=[], allow_delegation=False) crew = Crew(agents=[researcher, ops, writer], tasks=[])

researcher never holds shell_tool — but allow_delegation=True plus co-membership in the same Crew as ops means it can hand work off to an agent that does. This scanner reports:

🔴 CRITICAL  Transitive excessive agency: reaches shell_tool

Path: Researcher → Ops Engineer → shell_tool

🔴 CRITICAL Direct excessive agency: reaches shell_tool Path: Ops Engineer → shell_tool

writer — no tools, no delegation — gets no finding at all.


Configuration (guardbee.yml)

agent-graph-auditor:

fail-on: high # any | critical | high | medium | none max-files: 2000 exclude:

  • ".test.py"

Limitations (by design)

  • Single-file scope. Each file's graph is extracted independently — Python variable references don't resolve across files. A Crew(agents=[researcher, ops]) call will only connect to Agent(...) definitions found in the *same file*, even when scanning a whole directory.
  • CrewAI/AutoGen extraction is heuristic, not a real Python parser — constructor bodies are captured with a bounded text window rather than exact paren-matching (the same tradeoff ai-code-scanner's pattern-window matching makes), and it doesn't track reassignment, conditional agent construction, or dynamically built tool lists.
  • AutoGen's `speaker_selection_method` isn't modeled — GroupChat co-membership is always treated as a bidirectional reachability edge, which is the conservative (over-inclusive, not under-inclusive) assumption.
  • Capability classification is name-based. A tool named search_tool that secretly shells out internally won't be flagged; a tool named shell_tool_disabled will be. Review findings; don't take them as ground truth.
  • Only Python source is scanned in v1 — LangChain/LangGraph's JS/TS API isn't covered yet.

Development

npm run build

npm test # 31 unit tests


License

MIT — GuardBee

Feedback

Questions, ideas, or a false positive? Start a thread in GitHub Discussions or open an issue. If GuardBee is useful to you, a ⭐ on the repository helps others find it.

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