Bring Real Time Monitoring
to Pydantic AI
Learn how to connect Netdata to Pydantic AI and start using 10 AI agent tools in minutes. Fully managed, enterprise secure, and ready to use without writing a single line of code.
GDPR Free for SubscribersCompatible with every major AI agent and IDE
Gemini
What is the Netdata MCP Server?
Connect your Netdata monitoring infrastructure to any AI agent for instant, real-time observability and performance analysis through natural language.
What you can do
- Real-time Metrics — Fetch granular data from specific charts (CPU, RAM, Disk, Network) using
get_chart_datato diagnose performance bottlenecks. - Agent Health — Inspect node versions, host information, and enabled features with
get_agent_infoandlist_charts. - Alert Management — Query active alarms on local agents via
get_alarmsor monitor space-wide critical issues usinglist_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.
How it works
- Subscribe to this server
- Enter your Netdata Cloud Token or Agent URL
- Start monitoring your infrastructure from Claude, Cursor, or any MCP-compatible client
No more jumping between dashboards to find which node is spiking. Your AI acts as a 24/7 SRE or System Administrator.
Who is this for?
- DevOps Engineers — instantly correlate system alerts with recent deployments without leaving the terminal or IDE.
- SREs — automate the retrieval of chart data and alarm statuses to speed up incident response.
- System Administrators — manage large-scale node environments by querying spaces and rooms via simple conversation.
Built-in capabilities (10)
Get Netdata Agent information
Get current status of all configured alarms
Get all metrics for scraping
Fetch metric data from a specific chart
). List all available charts on the node
List nodes within a specific room
List rooms within a specific space
Fetch active alerts across the space
List all nodes connected to a space
List all Netdata Cloud spaces
Why Pydantic AI?
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.
- —
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 in Pydantic AI
Netdata and 4,000+ other MCP servers. One platform. One governance layer.
Teams that connect Netdata to Pydantic AI through Vinkius don't need to source, host, or maintain individual MCP servers. Every tool call runs inside a hardened runtime with credential isolation, DLP, and a signed audit chain.
Raw MCP | Vinkius | |
|---|---|---|
| Server catalog | Find and host yourself | 4,000+ managed |
| Infrastructure | Self-hosted | Sandboxed V8 isolates |
| Credential handling | Plaintext in config | Vault + runtime injection |
| Data loss prevention | None | Configurable DLP policies |
| Kill switch | None | Global instant shutdown |
| Financial circuit breakers | None | Per-server limits + alerts |
| Audit trail | None | Ed25519 signed logs |
| SIEM log streaming | None | Splunk, Datadog, Webhook |
| Honeytokens | None | Canary alerts on leak |
| Custom domains | Not applicable | DNS challenge verified |
| GDPR compliance | Manual effort | Automated purge + export |
Why teams choose Vinkius for Netdata in Pydantic AI
The Netdata 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. All 10 tools execute in hardened sandboxes optimized for native MCP execution.
Your AI agents in Pydantic AI only access the data you authorize, with DLP that blocks sensitive information from ever reaching the model, kill switch for instant shutdown, and up to 60% token savings. Enterprise-grade infrastructure, zero maintenance.
* 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
How Vinkius secures
Netdata for Pydantic AI
Every tool call from Pydantic AI to the Netdata MCP Server is protected by DLP redaction, cryptographic audit chains, V8 sandbox isolation, kill switch, and financial circuit breakers.
Frequently asked questions
How can I fetch specific metric data for a chart like CPU usage?
Use the get_chart_data tool. You need to provide the chart ID (e.g., 'system.cpu') and optionally specify time ranges like after or aggregation methods like group.
Can I see all active alerts across my entire Netdata Cloud space?
Yes! Use the list_space_alerts tool with your space_id. It will return all active alerts across all nodes connected to that specific Cloud space.
How do I list all the nodes available in a specific room?
Use the list_room_nodes tool. You will need to provide both the space_id and the room_id to filter the nodes within that specific grouping.
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.
MCPServerHTTP not found
Update: pip install --upgrade pydantic-ai
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