How to Use the Netdata MCP in Pydantic AI

Q: Can I use local models to parse infrastructure alerts?

The framework is entirely model-agnostic. You can route getalarms data to a local Llama 3 instance or send it to Claude, and the validation rules remain exactly the same.

Enforce strict type safety on Netdata telemetry using Pydantic AI runtime validation.

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Connect Netdata MCP to Pydantic AI

Create your Vinkius account to connect Netdata to Pydantic AI 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 Pydantic AI

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Claude

Perplexity

Type-Safe Netdata MCP Server

The Netdata MCP server delivers infrastructure telemetry that Pydantic AI validates at runtime. Every response from `get_chart_data` must match your predefined data models, or the agent throws an immediate validation error. This strictness prevents silent corruption in your monitoring pipelines. You connect via MCPToolset using the unified HTTP approach, ensuring your model-agnostic agent only acts on perfectly formatted CPU and memory metrics.

Deterministic Alert Parsing

Hallucinated incident data causes catastrophic downtime. Your MCP agent pulls active incidents using `list_space_alerts` and parses them into strict Pydantic classes before triggering any automated remediation. If the API returns an unexpected field from `get_alarms`, execution stops loudly. You fix the schema mismatch instead of letting a rogue agent shut down healthy production nodes based on malformed JSON.

Reliable Topology Discovery

Mapping distributed environments requires exact node identification. The agent traverses your infrastructure by calling `list_spaces` and `list_rooms`, validating every room ID before proceeding. It then fetches the underlying machines using `list_room_nodes` and `list_space_nodes`. Because every MCP step is type-checked, your multi-agent workflows never attempt to query non-existent servers with `get_agent_info`.

Setup guide

Set up Netdata MCP in Pydantic AI

Prerequisites

Python 3.10+ installed
pydantic-ai-slim[fastmcp] package
Active Vinkius subscription with a valid endpoint token

1

Install Pydantic AI with FastMCP
Run pip install "pydantic-ai-slim[fastmcp]". The FastMCP toolset replaces the deprecated MCPServerHTTP class with full protocol support.
2

Configure the FastMCPToolset
Pass a JSON-style config dict to FastMCPToolset with your Vinkius URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports Streamable HTTP, SSE, and Stdio transports.
3

Create and run your agent
Pass the toolset to Agent(toolsets=[toolset]) and call agent.run(). Swap openai:gpt-4o for any supported model — Anthropic, Google, Mistral, or Groq.

agent.py

from pydantic_ai import Agent
from pydantic_ai.toolsets.fastmcp import FastMCPToolset

toolset = FastMCPToolset({
    "mcpServers": {
        "netdata-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

agent = Agent(
    "openai:gpt-4o",
    toolsets=[toolset],
    system_prompt="You have access to Netdata tools.",
)

result = await agent.run("List recent Netdata transactions")
print(result.output)

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Common questions about Netdata MCP in Pydantic AI

Install the slim package with MCP extras. Create an MCPToolset pointing to your HTTP endpoint and pass it directly to the toolsets argument of your Agent.

The framework raises a validation error immediately. Your agent will not process the output of `get_all_metrics` if it violates your established Pydantic schema.

The framework is entirely model-agnostic. You can route `get_alarms` data to a local Llama 3 instance or send it to Claude, and the validation rules remain exactly the same.

The library consolidated its connection logic. You must use the unified MCPToolset class to handle Streamable HTTP or SSE transports when hooking up this specific server.

The server reads system load averages, memory consumption, and active node alerts. Vinkius isolates this data processing entirely within ephemeral sandboxes, meaning your telemetry is wiped from memory the second the connection terminates.

Use it with your favorite AI tools

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

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