Netdata MCP Server for Pydantic AIGive Pydantic AI instant access to 10 tools to Get Agent Info, Get Alarms, Get All Metrics, and more

Name: Netdata
Price: 29 USD
Availability: InStock
Rating: 5.0 (1 reviews)
Author: Vinkius

GDPR Free for Subscribers

Pydantic AI brings type-safe agent development to Python with first-class MCP support. Connect Netdata through Vinkius and every tool is automatically validated against Pydantic schemas. catch errors at build time, not in production.

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The Netdata MCP Server for Pydantic AI is a standout in the Cloud Infrastructure category — giving your AI agent 10 tools to work with, ready to go from day one.

Built for AI Agents by Vinkius

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python

import asyncio
from pydantic_ai import Agent
from pydantic_ai.mcp import MCPServerHTTP

async def main():
    # Your Vinkius token. get it at cloud.vinkius.com
    server = MCPServerHTTP(url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")

    agent = Agent(
        model="openai:gpt-4o",
        mcp_servers=[server],
        system_prompt=(
            "You are an assistant with access to Netdata "
            "(10 tools)."
        ),
    )

    result = await agent.run(
        "What tools are available in Netdata?"
    )
    print(result.data)

asyncio.run(main())

Fully ManagedVinkius Servers

60%Token savings

High SecurityEnterprise-grade

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V8 IsolateSandboxed

Ed25519Audit chain

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Stream every event to Splunk, Datadog, or your own webhook in real-time

* 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 Netdata MCP Server

Connect your Netdata monitoring infrastructure to any AI agent for instant, real-time observability and performance analysis through natural language.

Pydantic AI validates every Netdata tool response against typed schemas, catching data inconsistencies at build time. Connect 10 tools through Vinkius and switch between OpenAI, Anthropic, or Gemini without changing your integration code. full type safety, structured output guarantees, and dependency injection for testable agents.

What you can do

Real-time Metrics — Fetch granular data from specific charts (CPU, RAM, Disk, Network) using get_chart_data to diagnose performance bottlenecks.
Agent Health — Inspect node versions, host information, and enabled features with get_agent_info and list_charts.
Alert Management — Query active alarms on local agents via get_alarms or monitor space-wide critical issues using list_space_alerts.
Cloud Orchestration — Navigate your entire infrastructure by listing spaces, rooms, and nodes connected to Netdata Cloud.
Scraping & Export — Retrieve all metrics in a format suitable for external analysis tools using get_all_metrics.

The Netdata MCP Server exposes 10 tools through the Vinkius. Connect it to Pydantic AI in under two minutes — credentials fully managed, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.

All 10 Netdata tools available for Pydantic AI

When Pydantic AI connects to Netdata through Vinkius, your AI agent gets direct access to every tool listed below — spanning real-time-monitoring, infrastructure-observability, system-metrics, and more. Every call runs in a secure, isolated environment with full audit visibility. Beyond a simple connection, you get real-time monitoring of agent activity, enterprise governance, and optimized token usage.

get

Get agent info on Netdata

Get Netdata Agent information

get

Get alarms on Netdata

Get current status of all configured alarms

get

Get all metrics on Netdata

Get all metrics for scraping

get

Get chart data on Netdata

Fetch metric data from a specific chart

list

List charts on Netdata

). List all available charts on the node

list

List room nodes on Netdata

List nodes within a specific room

list

List rooms on Netdata

List rooms within a specific space

list

List space alerts on Netdata

Fetch active alerts across the space

list

List space nodes on Netdata

List all nodes connected to a space

list

List spaces on Netdata

List all Netdata Cloud spaces

Connect Netdata to Pydantic AI via MCP

Follow these steps to wire Netdata into Pydantic AI. The entire setup takes under two minutes — your credentials stay safe behind Vinkius.

Install Pydantic AI

Run pip install pydantic-ai

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 Netdata with type-safe schemas

Why Use Pydantic AI with the Netdata MCP Server

Pydantic AI provides unique advantages when paired with Netdata through the Model Context Protocol.

Full type safety: every MCP tool response is validated against Pydantic models, catching data inconsistencies before they reach your application

Model-agnostic architecture. switch between OpenAI, Anthropic, or Gemini without changing your Netdata integration code

Structured output guarantee: Pydantic AI ensures tool results conform to defined schemas, eliminating runtime type errors

Dependency injection system cleanly separates your Netdata connection logic from agent behavior for testable, maintainable code

Netdata + Pydantic AI Use Cases

Practical scenarios where Pydantic AI combined with the Netdata MCP Server delivers measurable value.

Type-safe data pipelines: query Netdata with guaranteed response schemas, feeding validated data into downstream processing

API orchestration: chain multiple Netdata tool calls with Pydantic validation at each step to ensure data integrity end-to-end

Production monitoring: build validated alert agents that query Netdata and output structured, schema-compliant notifications

Testing and QA: use Pydantic AI's dependency injection to mock Netdata responses and write comprehensive agent tests

Example Prompts for Netdata in Pydantic AI

Ready-to-use prompts you can give your Pydantic AI agent to start working with Netdata immediately.

"Get the current information and version of the Netdata agent."

"List all available charts on this node so I can see what metrics are being collected."

"Are there any active alarms or warnings on the local agent right now?"

Troubleshooting Netdata MCP Server with Pydantic AI

Common issues when connecting Netdata to Pydantic AI through Vinkius, and how to resolve them.

MCPServerHTTP not found

Update: pip install --upgrade pydantic-ai

Netdata + Pydantic AI FAQ

Common questions about integrating Netdata MCP Server with Pydantic AI.

How does Pydantic AI discover MCP tools?

Create an MCPServerHTTP instance with the server URL. Pydantic AI connects, discovers all tools, and generates typed Python interfaces automatically.

Does Pydantic AI validate MCP tool responses?

Yes. When you define result types as Pydantic models, every tool response is validated against the schema. Invalid data raises a clear error instead of silently corrupting your pipeline.

Can I switch LLM providers without changing MCP code?

Absolutely. Pydantic AI abstracts the model layer. your Netdata MCP integration works identically with OpenAI, Anthropic, Google, or any supported provider.

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