Kandji MCP Server for LangChain 10 tools — connect in under 2 minutes
LangChain is the leading Python framework for composable LLM applications. Connect Kandji through Vinkius and LangChain agents can call every tool natively. combine them with retrievers, memory, and output parsers for sophisticated AI pipelines.
ASK AI ABOUT THIS MCP SERVER
Vinkius supports streamable HTTP and SSE.
import asyncio
from langchain_mcp_adapters.client import MultiServerMCPClient
from langchain_openai import ChatOpenAI
from langgraph.prebuilt import create_react_agent
async def main():
# Your Vinkius token. get it at cloud.vinkius.com
async with MultiServerMCPClient({
"kandji": {
"transport": "streamable_http",
"url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp",
}
}) as client:
tools = client.get_tools()
agent = create_react_agent(
ChatOpenAI(model="gpt-4o"),
tools,
)
response = await agent.ainvoke({
"messages": [{
"role": "user",
"content": "Using Kandji, show me what tools are available.",
}]
})
print(response["messages"][-1].content)
asyncio.run(main())
* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure
About Kandji MCP Server
Empower your AI agents with Kandji's modern Apple MDM platform. This MCP server allows you to list and retrieve device details, manage blueprints and custom apps, track administrative activity, and view system security parameters directly through the Kandji API. Ideal for automating IT operations and fleet security for macOS and iOS.
LangChain's ecosystem of 500+ components combines seamlessly with Kandji through native MCP adapters. Connect 10 tools via Vinkius and use ReAct agents, Plan-and-Execute strategies, or custom agent architectures. with LangSmith tracing giving full visibility into every tool call, latency, and token cost.
The Kandji MCP Server exposes 10 tools through the Vinkius. Connect it to LangChain in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.
How to Connect Kandji to LangChain via MCP
Follow these steps to integrate the Kandji MCP Server with LangChain.
Install dependencies
Run pip install langchain langchain-mcp-adapters langgraph langchain-openai
Replace the token
Replace [YOUR_TOKEN_HERE] with your Vinkius token
Run the agent
Save the code and run python agent.py
Explore tools
The agent discovers 10 tools from Kandji via MCP
Why Use LangChain with the Kandji MCP Server
LangChain provides unique advantages when paired with Kandji through the Model Context Protocol.
The largest ecosystem of integrations, chains, and agents. combine Kandji MCP tools with 500+ LangChain components
Agent architecture supports ReAct, Plan-and-Execute, and custom strategies with full MCP tool access at every step
LangSmith tracing gives you complete visibility into tool calls, latencies, and token usage for production debugging
Memory and conversation persistence let agents maintain context across Kandji queries for multi-turn workflows
Kandji + LangChain Use Cases
Practical scenarios where LangChain combined with the Kandji MCP Server delivers measurable value.
RAG with live data: combine Kandji tool results with vector store retrievals for answers grounded in both real-time and historical data
Autonomous research agents: LangChain agents query Kandji, synthesize findings, and generate comprehensive research reports
Multi-tool orchestration: chain Kandji tools with web scrapers, databases, and calculators in a single agent run
Production monitoring: use LangSmith to trace every Kandji tool call, measure latency, and optimize your agent's performance
Kandji MCP Tools for LangChain (10)
These 10 tools become available when you connect Kandji to LangChain via MCP:
get_device
Essential for deep-dive auditing of a specific asset. Retrieves details for a specific device
get_organization
Use to verify account identity. Retrieves details about your Kandji organization
list_activity
Essential for auditing system changes and recent management history. Lists recent management activity
list_auto_apps
Essential for auditing standard software libraries. Lists all Kandji Auto Apps
list_blueprints
Useful for understanding how devices are categorized and configured. Lists all device blueprints
list_commands
g., Lock, Wipe, Restart) sent to managed devices. Useful for auditing remote actions. Lists recent MDM commands sent to devices
list_custom_apps
Useful for auditing non-store software deployments. Lists all custom applications
list_devices
Returns device names, IDs, and OS versions. Use this as the main tool for auditing the device fleet. Lists all managed Apple devices in Kandji
list_parameters
Useful for auditing available security controls. Lists all library parameters (policies)
list_users
Useful for identifying device owners and primary users. Lists all users associated with devices
Example Prompts for Kandji in LangChain
Ready-to-use prompts you can give your LangChain agent to start working with Kandji immediately.
"List all managed Mac computers in Kandji."
"Show me the details for device ID 'abc-123'."
"Check recent administrative activity in Kandji."
Troubleshooting Kandji MCP Server with LangChain
Common issues when connecting Kandji to LangChain through the Vinkius, and how to resolve them.
MultiServerMCPClient not found
pip install langchain-mcp-adaptersKandji + LangChain FAQ
Common questions about integrating Kandji MCP Server with LangChain.
How does LangChain connect to MCP servers?
langchain-mcp-adapters to create an MCP client. LangChain discovers all tools and wraps them as native LangChain tools compatible with any agent type.Which LangChain agent types work with MCP?
Can I trace MCP tool calls in LangSmith?
Connect Kandji with your favorite client
Step-by-step setup guides for every MCP-compatible client and framework:
Anthropic's native desktop app for Claude with built-in MCP support.
AI-first code editor with integrated LLM-powered coding assistance.
GitHub Copilot in VS Code with Agent mode and MCP support.
Purpose-built IDE for agentic AI coding workflows.
Autonomous AI coding agent that runs inside VS Code.
Anthropic's agentic CLI for terminal-first development.
Python SDK for building production-grade OpenAI agent workflows.
Google's framework for building production AI agents.
Type-safe agent development for Python with first-class MCP support.
TypeScript toolkit for building AI-powered web applications.
TypeScript-native agent framework for modern web stacks.
Python framework for orchestrating collaborative AI agent crews.
Leading Python framework for composable LLM applications.
Data-aware AI agent framework for structured and unstructured sources.
Microsoft's framework for multi-agent collaborative conversations.
Connect Kandji to LangChain
Get your token, paste the configuration, and start using 10 tools in under 2 minutes. No API key management needed.