How to Use the NetBird MCP in LlamaIndex

Q: How do I connect LlamaIndex to my NetBird account?

Install the llama-index-tools-mcp library and create a BasicMCPClient with your Vinkius URL. Then, pass that client to McpToolSpec to generate a list of LlamaIndex tools for your agent.

Q: Can I limit which NetBird tools my LlamaIndex agent can use?

Yes. The McpToolSpec constructor accepts an allowedtools argument. You can pass a list of specific tool names, like 'listpeers', 'getpolicy', to restrict the agent to read-only operations.

Q: How can I check network traffic history for NetBird with my LlamaIndex app?

You can create an agent that periodically calls listnetworktrafficevents and indexes the results. This builds a historical record in your vector database that you can then query for traffic patterns, connection attempts, or specific source/destination activity.

Build a queryable knowledge base of your NetBird configuration with LlamaIndex and live API data.

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Connect NetBird MCP to LlamaIndex

Create your Vinkius account to connect NetBird to LlamaIndex and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

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Setup NetBird with LlamaIndex

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Ask Questions About Your Live Network State

This isn't just about calling an API. This MCP Server lets your LlamaIndex application ingest the current state of your network. Your agent can periodically run `list_peers`, `list_policies`, and `list_groups`, then index the results into a vector store. Now you can build a RAG application that answers questions with grounded facts. Ask it things like, "Which policy governs access to the production database?" or "List all peers in the Frankfurt region." It finds the answer in the indexed data, not a hallucination.

Index and Search Your NetBird Security Audit Logs

Use a LlamaIndex agent to build a searchable history of every action in your account. Set it up to run `list_audit_events` and `list_network_traffic_events` on a schedule. The agent automatically indexes the new events as they happen. This creates a powerful security analysis tool. You can query your index with natural language: "Show me all login failures from last Tuesday" or "What changes did bob@example.com make this month?" The answers are pulled directly from your logs.

Ground LlamaIndex Agents in Your NetBird Reality

An agent that acts without context is dangerous. Use this MCP Server to give your agent a complete picture of your network before it does anything. Have it run `list_posture_checks`, `list_nameservers`, and `list_networks` to build an initial index. When you ask the agent to "add a new server to the monitoring group," it first queries its knowledge base to find the correct group ID and understand existing policies. Only then does it call `create_peer` and `update_group`, preventing costly configuration mistakes.

Setup guide

Set up NetBird MCP in LlamaIndex

Prerequisites

Python 3.10+ installed
llama-index-tools-mcp package
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai. The MCP tools package provides BasicMCPClient and McpToolSpec.
2

Connect with BasicMCPClient
Point BasicMCPClient to your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports SSE and Streamable HTTP transports.
3

Convert to LlamaIndex tools
Call mcp_tool_spec.to_tool_list_async() to convert all NetBird MCP tools into native FunctionTool objects that any LlamaIndex agent can use.
4

Run with any LLM
Create a FunctionAgent with the tools and your preferred LLM. Swap OpenAI for Anthropic, Gemini, or any LlamaIndex-supported provider.

agent.py

from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI

# Connect to the MCP
mcp_client = BasicMCPClient(
    "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)
mcp_tool_spec = McpToolSpec(client=mcp_client)

# Convert MCP tools to LlamaIndex tools
tools = await mcp_tool_spec.to_tool_list_async()

# Create and run the agent
agent = FunctionAgent(
    tools=tools,
    llm=OpenAI(model="gpt-4o"),
    system_prompt="You have access to NetBird tools.",
)
response = await agent.run("List recent NetBird data")

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Common questions about NetBird MCP in LlamaIndex

Install the `llama-index-tools-mcp` library and create a `BasicMCPClient` with your Vinkius URL. Then, pass that client to `McpToolSpec` to generate a list of LlamaIndex tools for your agent.

Yes. The `McpToolSpec` constructor accepts an `allowed_tools` argument. You can pass a list of specific tool names, like `['list_peers', 'get_policy']`, to restrict the agent to read-only operations.

The key is memory. LlamaIndex excels at building a searchable knowledge base from tool outputs. Instead of just getting a one-time list of peers, you can index that data and ask complex questions about your NetBird setup over time.

You can create an agent that periodically calls `list_network_traffic_events` and indexes the results. This builds a historical record in your vector database that you can then query for traffic patterns, connection attempts, or specific source/destination activity.

This MCP server touches your NetBird network policies, audit logs, and user lists. Because LlamaIndex indexes tool outputs, this data may be stored in your own vector database. The connection from Vinkius to NetBird is always direct and ephemeral, but you are responsible for securing the indexed data.

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