Kandji MCP Server for LlamaIndex 10 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Kandji as an MCP tool provider through the Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.
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Vinkius supports streamable HTTP and SSE.
import asyncio
from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI
async def main():
# Your Vinkius token — get it at cloud.vinkius.com
mcp_client = BasicMCPClient("https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
mcp_tool_spec = McpToolSpec(client=mcp_client)
tools = await mcp_tool_spec.to_tool_list_async()
agent = FunctionAgent(
tools=tools,
llm=OpenAI(model="gpt-4o"),
system_prompt=(
"You are an assistant with access to Kandji. "
"You have 10 tools available."
),
)
response = await agent.run(
"What tools are available in Kandji?"
)
print(response)
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.
LlamaIndex agents combine Kandji tool responses with indexed documents for comprehensive, grounded answers. Connect 10 tools through the Vinkius and query live data alongside vector stores and SQL databases in a single turn — ideal for hybrid search, data enrichment, and analytical workflows.
The Kandji MCP Server exposes 10 tools through the Vinkius. Connect it to LlamaIndex 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 LlamaIndex via MCP
Follow these steps to integrate the Kandji MCP Server with LlamaIndex.
Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai
Replace the token
Replace [YOUR_TOKEN_HERE] with your Vinkius token
Run the agent
Save to agent.py and run: python agent.py
Explore tools
The agent discovers 10 tools from Kandji
Why Use LlamaIndex with the Kandji MCP Server
LlamaIndex provides unique advantages when paired with Kandji through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Kandji tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Kandji tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Kandji, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Kandji tools were called, what data was returned, and how it influenced the final answer
Kandji + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Kandji MCP Server delivers measurable value.
Hybrid search: combine Kandji real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Kandji to augment indexed data with live information before generating user-facing responses
Knowledge base agents: build agents that maintain and update knowledge bases by periodically querying Kandji for fresh data
Analytical workflows: chain Kandji queries with LlamaIndex's data connectors to build multi-source analytical reports
Kandji MCP Tools for LlamaIndex (10)
These 10 tools become available when you connect Kandji to LlamaIndex 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 LlamaIndex
Ready-to-use prompts you can give your LlamaIndex 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 LlamaIndex
Common issues when connecting Kandji to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpKandji + LlamaIndex FAQ
Common questions about integrating Kandji MCP Server with LlamaIndex.
How does LlamaIndex connect to MCP servers?
Can I combine MCP tools with vector stores?
Does LlamaIndex support async MCP calls?
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 LlamaIndex
Get your token, paste the configuration, and start using 10 tools in under 2 minutes. No API key management needed.