Jamf Pro 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 Jamf Pro as an MCP tool provider through 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 Jamf Pro. "
"You have 10 tools available."
),
)
response = await agent.run(
"What tools are available in Jamf Pro?"
)
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 Jamf Pro MCP Server
Empower your AI agents to manage your Apple ecosystem with Jamf Pro. This MCP server allows you to list mobile devices and computers, track inventory details, manage users and buildings, and view management scripts and packages directly through the Jamf Pro API. Ideal for automating IT operations and device management.
LlamaIndex agents combine Jamf Pro tool responses with indexed documents for comprehensive, grounded answers. Connect 10 tools through 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 Jamf Pro 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 Jamf Pro to LlamaIndex via MCP
Follow these steps to integrate the Jamf Pro 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 Jamf Pro
Why Use LlamaIndex with the Jamf Pro MCP Server
LlamaIndex provides unique advantages when paired with Jamf Pro through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Jamf Pro tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Jamf Pro tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Jamf Pro, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Jamf Pro tools were called, what data was returned, and how it influenced the final answer
Jamf Pro + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Jamf Pro MCP Server delivers measurable value.
Hybrid search: combine Jamf Pro real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Jamf Pro 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 Jamf Pro for fresh data
Analytical workflows: chain Jamf Pro queries with LlamaIndex's data connectors to build multi-source analytical reports
Jamf Pro MCP Tools for LlamaIndex (10)
These 10 tools become available when you connect Jamf Pro to LlamaIndex via MCP:
get_computer
Includes disk encryption status, installed apps, and user info. Use this for detailed Mac auditing. Retrieves details for a specific computer
get_mobile_device
Includes OS version, security status, and assigned user. Use this for deep investigation of a specific mobile asset. Retrieves details for a specific mobile device
list_buildings
Useful for auditing site-based device distribution. Lists all configured buildings
list_categories
Useful for navigating the management hierarchy. Lists all management categories
list_computers
Includes computer names, IDs, and serial numbers. Essential for auditing the Mac fleet. Lists all managed computers
list_departments
Use this to analyze device distribution by business unit. Lists all configured departments
list_mobile_devices
Returns device names, IDs, and models. Use this to audit the mobile device fleet. Lists all managed mobile devices
list_packages
pkg, .dmg) available in the Jamf Pro distribution points. Useful for auditing available software deployments. Lists all software packages
list_scripts
) stored in Jamf Pro for remote execution. Useful for auditing custom automation assets. Lists all management scripts
list_users
Useful for identifying which users are associated with specific devices. Lists all users in the system
Example Prompts for Jamf Pro in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with Jamf Pro immediately.
"List all computers managed in Jamf Pro."
"Show me details for mobile device ID '456'."
"List all management scripts configured in the system."
Troubleshooting Jamf Pro MCP Server with LlamaIndex
Common issues when connecting Jamf Pro to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpJamf Pro + LlamaIndex FAQ
Common questions about integrating Jamf Pro 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 Jamf Pro 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 Jamf Pro to LlamaIndex
Get your token, paste the configuration, and start using 10 tools in under 2 minutes. No API key management needed.