How to Use the Netdata MCP in LlamaIndex

Q: How do I initialize the Netdata MCP tools in LlamaIndex?

Install llama-index-tools-mcp. Set up a BasicMCPClient, wrap it with McpToolSpec, and call totoollistasync() to feed the functions into your FunctionAgent.

Q: Can LlamaIndex filter which Netdata tools the agent uses?

Yes. Use the allowedtools filter when building the tool array. You might restrict an agent to just listcharts and getchartdata to prevent it from dumping the entire alarm database.

Q: How does LlamaIndex handle large metric payloads from Netdata?

The agent chunks the JSON output from getallmetrics before indexing. This ensures the vector store can retrieve specific data points without blowing up your context window.

Turn your infrastructure metrics into a searchable knowledge base. LlamaIndex queries this MCP Server and grounds RAG apps in live system data.

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Connect Netdata MCP to LlamaIndex

Create your Vinkius account to connect Netdata 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 Netdata with LlamaIndex

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Index Live Netdata MCP Server Metrics

Static runbooks are useless during a live incident. You need RAG applications grounded in actual system state. LlamaIndex pulls live node statistics via `get_agent_info` and vectors them into your knowledge base. When a database slows down, you don't guess. You query the index. The `get_chart_data` tool fetches the raw disk I/O metrics, and the engine synthesizes an answer based on current throughput, not yesterday's logs.

Semantic Search for Infrastructure Alerts

Alert fatigue happens when you lose historical context. By combining `list_space_alerts` with LlamaIndex, you build a searchable history of every triggered alarm across your organization. An engineer asks the chat interface about recent memory spikes in the production room. The agent runs `list_room_nodes`, then cross-references `get_alarms` output. You get a precise summary of which nodes failed and when.

Map Cloud Topologies for RAG

Your AI needs to understand your physical architecture to give accurate answers. The `list_spaces` and `list_rooms` tools feed organizational structure directly into the document store. This prevents hallucinations. If someone asks about the staging environment, LlamaIndex restricts its data retrieval to nodes found via `list_space_nodes` for that specific space ID. The context remains strictly bounded.

Setup guide

Set up Netdata 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 Netdata 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 Netdata tools.",
)
response = await agent.run("List recent Netdata data")

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Common questions about Netdata MCP in LlamaIndex

Install `llama-index-tools-mcp`. Set up a `BasicMCPClient`, wrap it with `McpToolSpec`, and call `to_tool_list_async()` to feed the functions into your `FunctionAgent`.

Yes. Use the `allowed_tools` filter when building the tool array. You might restrict an agent to just `list_charts` and `get_chart_data` to prevent it from dumping the entire alarm database.

The agent chunks the JSON output from `get_all_metrics` before indexing. This ensures the vector store can retrieve specific data points without blowing up your context window.

Dashboards require human interpretation. RAG combines the raw telemetry with your internal runbooks. You ask a question, and the engine uses live chart data to give you the exact command to fix the issue.

The MCP server operates in a zero-trust environment. Only the specific CPU, memory, and alert data you request is processed. Vinkius drops the connection and wipes the memory space immediately after the index update completes.

Use it with your favorite AI tools

Connect this server to Cursor, Claude, VS Code, and more.

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