Airbrake MCP Server for Pydantic AIGive Pydantic AI instant access to 10 tools to Check Airbrake Status, Get Error Group, Get Project, and more
Pydantic AI brings type-safe agent development to Python with first-class MCP support. Connect Airbrake through Vinkius and every tool is automatically validated against Pydantic schemas. catch errors at build time, not in production.
Ask AI about this App Connector for Pydantic AI
The Airbrake app connector for Pydantic AI is a standout in the Productivity category — giving your AI agent 10 tools to work with, ready to go from day one.
Vinkius delivers Streamable HTTP and SSE to any MCP client
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 Airbrake "
"(10 tools)."
),
)
result = await agent.run(
"What tools are available in Airbrake?"
)
print(result.data)
asyncio.run(main())
* 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 Airbrake MCP Server
Connect your Airbrake account to any AI agent and manage your entire error monitoring workflow through natural conversation.
Pydantic AI validates every Airbrake 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
- Project Management — List all monitored projects and fetch detailed project configuration
- Error Group Analysis — List error groups by project with occurrence counts, severity, and last-seen timestamps
- Deep Error Inspection — Inspect individual error groups with full stack traces, affected users, and frequency data
- Error Notices — List individual error occurrences within a group and report custom errors for tracking
- Deployment Tracking — List all tracked Installments and record new releases with version and environment info
- Environment Overview — View all configured environments (production, staging, development) per project
- Health Check — Verify API connectivity and account status
The Airbrake MCP Server exposes 10 tools through the Vinkius. Connect it to Pydantic AI in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.
All 10 Airbrake tools available for Pydantic AI
When Pydantic AI connects to Airbrake through Vinkius, your AI agent gets direct access to every tool listed below — spanning airbrake, error-monitoring-api, exception-tracking, and more. Every call is secured with network, filesystem, subprocess, and code evaluation entitlements inside a sandboxed runtime. Beyond a simple connection, you get a full AI Gateway with real-time visibility into agent activity, enterprise governance, and optimized token usage.
Verify API connectivity
Get error group details
Get project details
List deployments
List environments
List error groups
List error notices
List all projects
Report an error notice
Track a deployment
Connect Airbrake to Pydantic AI via MCP
Follow these steps to wire Airbrake into Pydantic AI. The entire setup takes under two minutes — your credentials stay safe behind the Vinkius.
Install Pydantic AI
pip install pydantic-aiReplace the token
[YOUR_TOKEN_HERE] with your Vinkius tokenRun the agent
agent.py and run: python agent.pyExplore tools
Why Use Pydantic AI with the Airbrake MCP Server
Pydantic AI provides unique advantages when paired with Airbrake 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 Airbrake integration code
Structured output guarantee: Pydantic AI ensures tool results conform to defined schemas, eliminating runtime type errors
Dependency injection system cleanly separates your Airbrake connection logic from agent behavior for testable, maintainable code
Airbrake + Pydantic AI Use Cases
Practical scenarios where Pydantic AI combined with the Airbrake MCP Server delivers measurable value.
Type-safe data pipelines: query Airbrake with guaranteed response schemas, feeding validated data into downstream processing
API orchestration: chain multiple Airbrake tool calls with Pydantic validation at each step to ensure data integrity end-to-end
Production monitoring: build validated alert agents that query Airbrake and output structured, schema-compliant notifications
Testing and QA: use Pydantic AI's dependency injection to mock Airbrake responses and write comprehensive agent tests
Example Prompts for Airbrake in Pydantic AI
Ready-to-use prompts you can give your Pydantic AI agent to start working with Airbrake immediately.
"List all my Airbrake projects."
"Show me the error groups for project 120394."
"Record a Installment for project 120394: version 3.2.1, production."
Troubleshooting Airbrake MCP Server with Pydantic AI
Common issues when connecting Airbrake to Pydantic AI through the Vinkius, and how to resolve them.
MCPServerHTTP not found
pip install --upgrade pydantic-aiAirbrake + Pydantic AI FAQ
Common questions about integrating Airbrake MCP Server with Pydantic AI.
How does Pydantic AI discover MCP tools?
MCPServerHTTP instance with the server URL. Pydantic AI connects, discovers all tools, and generates typed Python interfaces automatically.