How to Use the HyperDX (Open Source Observability) MCP in Pydantic AI

Q: How do I ensure log data is correct in Pydantic AI with HyperDX (Open Source Observability)?

Every response from listlogs is checked against your Pydantic model at runtime. If the API returns invalid data, the system flags it immediately.

Q: Can Pydantic AI safely create alerts using this MCP Server?

Yes. Your agent uses createalert while your Pydantic models validate every argument before the call. This prevents runtime errors during deployment.

Get type-safe observability in Pydantic AI with this MCP Server for validated log and alert management.

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Connect HyperDX (Open Source Observability) MCP to Pydantic AI

Create your Vinkius account to connect HyperDX (Open Source Observability) 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 HyperDX (Open Source Observability) with Pydantic AI

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Type-validated log access for Pydantic AI

Get structured log data that matches your expected models. The agent uses `list_logs` to fetch events and validates the output against your Pydantic schemas. If the data format changes, the agent fails immediately, preventing corrupted state in your observability pipeline.

Correct alert configuration with this MCP Server

Ensure your alert rules stay consistent. Your agent uses `create_alert` to deploy new rules, with the tool response automatically validated against your internal models. This prevents malformed requests from ever reaching your production monitoring system.

Dashboard telemetry with strict validation

Inspect your dashboards with complete confidence. The agent retrieves metric details using `get_dashboard` and forces the response into your defined Pydantic types. This keeps your telemetry processing clean and free from unexpected field values.

Setup guide

Set up HyperDX (Open Source Observability) 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": {
        "hyperdx-open-source-observability-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

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

result = await agent.run("List recent HyperDX (Open Source Observability) transactions")
print(result.output)

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Common questions about HyperDX (Open Source Observability) MCP in Pydantic AI

Every response from `list_logs` is checked against your Pydantic model at runtime. If the API returns invalid data, the system flags it immediately.

Yes. Your agent uses `create_alert` while your Pydantic models validate every argument before the call. This prevents runtime errors during deployment.

It is. The toolset is designed to return structured data that maps directly to your models, ensuring no field is ever left unvalidated.

Install the MCP toolset package and pass the server URL to your agent's configuration. The framework handles the rest of the transport logic.

Your log data is never stored by the server. It only passes through during your active session, protected by your existing API authentication.

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