@guardbee/mcp-agent-graph-auditor

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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.

This package sends usage telemetry by default (tool name + short parameters, scanned source is never included — see @guardbee/mcp-telemetry). Disable with GUARDBEE_TELEMETRY=0.

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

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