GuardEntry MCP Server

Model Context Protocol (MCP) server for GuardEntry — exposes the Agent Policy Router (APR) as MCP tools so any MCP-compatible AI agent can gate its actions through your compliance policy before executing them.

Transports

Transport	Entry point	Use with
Streamable HTTP	npm run start:http	CrewAI, LangChain, any HTTP MCP client
stdio	npm start	Claude Desktop, Cursor, VS Code Cline

Quick start

git clone https://github.com/guardentryai/mcp-server.git
cd mcp-server
npm install

Create a .env file (copy from .env.example):

GUARDENTRY_API_KEY=ge_k1_your_key_here
# GUARDENTRY_BASE_URL=https://app.guardentry.ai   # default
# MCP_PORT=3001                                    # default (HTTP mode only)
# MCP_TOOLS=guardentry_evaluate_action            # optional tool allowlist

Get an API key at app.guardentry.ai → Settings → API Keys.

HTTP mode (CrewAI, LangChain, etc.)

npm run start:http
# GuardEntry APR MCP server listening on http://localhost:3001/mcp
# Health: http://localhost:3001/health

stdio mode (Claude Desktop, Cursor)

npm start

Claude Desktop config (~/Library/Application Support/Claude/claude_desktop_config.json):

{
  "mcpServers": {
    "guardentry": {
      "command": "npx",
      "args": ["--yes", "guardentry-mcp"],
      "env": { "GUARDENTRY_API_KEY": "ge_k1_your_key_here" }
    }
  }
}

Available tools

Tool	Description
guardentry_evaluate_action	Evaluate a proposed action — returns allow, block, or require_approval with reasoning
guardentry_chat	Natural language interface to GuardEntry
guardentry_list_risks	Query the risk register
guardentry_compliance_status	Get framework readiness (SOC 2, ISO 27001, NIST CSF…)
guardentry_invoke_agent	Run a GuardEntry agent by ID or type
guardentry_invoke_skill	Call a single agent skill directly
guardentry_list_skills	List available skills
guardentry_get_agent_policy	Get the effective policy for an agent
guardentry_confirm_policy	Promote an inferred policy to confirmed
guardentry_update_policy	Update policy rules (blocked actions, allowed tools, approval patterns)
guardentry_list_pending	List actions awaiting dashboard approval
guardentry_action_status	Check the status of a specific action

Limiting tools (recommended for CrewAI + Claude)

Anthropic's API has a ~16-parameter union-type limit across all active tools. Use MCP_TOOLS to expose only what you need:

MCP_TOOLS=guardentry_evaluate_action npm run start:http

CrewAI integration

from crewai import Agent, Task, Crew, LLM
from crewai.mcp.config import MCPServerHTTP
import os

mcp = MCPServerHTTP(
    url="http://localhost:3001/mcp",
    headers={"Authorization": f"Bearer {os.environ['GUARDENTRY_API_KEY']}"},
)

agent = Agent(
    role="Compliance Analyst",
    goal="Gate every action through GuardEntry APR before executing",
    backstory="You check compliance policy before any sensitive operation.",
    mcps=[mcp],
    llm=LLM(model="anthropic/claude-haiku-4-5-20251001",
             api_key=os.environ["ANTHROPIC_API_KEY"]),
)

Run the smoke test:

GUARDENTRY_API_KEY=ge_k1_... ANTHROPIC_API_KEY=sk-ant-... python test-crewai.py

Environment variables

Variable	Default	Description
GUARDENTRY_API_KEY	(required)	API key from GuardEntry dashboard
GUARDENTRY_BASE_URL	https://app.guardentry.ai	Override for local/staging
MCP_PORT	3001	HTTP server port
MCP_TOOLS	(all tools)	Comma-separated tool allowlist

License

MIT — see LICENSE

Links

• GuardEntry
• Integration docs
• Dashboard setup