How to Use the Airbrake MCP in Pydantic AI

Q: How do I add the Airbrake MCP Server to a Pydantic AI agent?

Install the library using pip install "pydantic-ai-slimmcp". Use the unified MCPToolset("http://...") constructor with your Vinkius endpoint, then pass it to the Pydantic AI agent's toolsets parameter.

Q: Can I run the Airbrake MCP server locally with Pydantic AI?

No. The Pydantic AI MCPToolset expects an externally running server. Vinkius hosts and manages the Airbrake server endpoint for you, so you only need to provide the HTTP URL to your agent.

Get type-safe Airbrake error tracking in your Pydantic AI workflows with strict runtime schema validation.

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Connect Airbrake MCP to Pydantic AI

Create your Vinkius account to connect Airbrake 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 Airbrake with Pydantic AI

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Type-safe Airbrake error triaging with Pydantic AI

Don't let your agent crash because an API payload changed. This MCP Server integrates with Pydantic AI to validate every schema returned by `get_error_group` and `list_error_groups` against strict runtime models. If the Airbrake API returns an unexpected null value or an undocumented field, the Pydantic AI framework raises a validation error immediately. You get absolute guarantees that your agent is working with clean, structured Airbrake data before it tries to write a fix.

Validate Airbrake deployment payloads using Pydantic AI

Avoid corrupting your release logs with malformed data. When your Pydantic AI agent uses `track_deploy` or `report_notice`, the framework validates the outgoing payload against the server's input schema before hitting the network. This client-side validation catches missing parameters or incorrect types early. It prevents useless HTTP requests and ensures your Airbrake project history remains accurate and properly formatted inside Pydantic AI.

Model-agnostic Airbrake project discovery and status checks

You can run your agent on local models or commercial APIs while safely querying your Airbrake monitoring stack. Use `check_airbrake_status` and `list_projects` to verify connections and map out your active codebases across different LLM providers. Because Pydantic AI handles the Airbrake tool definitions abstractly, you can switch your underlying model without rewriting your error-checking logic. The agent interacts with the exact same validated Airbrake schemas every time.

Setup guide

Set up Airbrake 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": {
        "airbrake-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

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

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

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Common questions about Airbrake MCP in Pydantic AI

Install the library using `pip install "pydantic-ai-slim[mcp]"`. Use the unified `MCPToolset("http://...")` constructor with your Vinkius endpoint, then pass it to the Pydantic AI agent's `toolsets` parameter.

Yes. Every time the Pydantic AI agent calls an Airbrake tool like `list_notices`, the response is validated against a Pydantic model at runtime. Any schema mismatch triggers an immediate validation failure.

No. The Pydantic AI `MCPToolset` expects an externally running server. Vinkius hosts and manages the Airbrake server endpoint for you, so you only need to provide the HTTP URL to your agent.

The framework supports both Streamable HTTP and SSE transports, allowing your Pydantic AI agent to maintain a persistent, low-latency connection to the Airbrake endpoints.

Your Airbrake API tokens are managed securely through Vinkius's zero-trust MCP gateway. Pydantic AI never exposes these credentials to the LLM; they are injected securely at the transport layer inside an ephemeral sandbox.

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