Can I acknowledge and resolve incidents directly from my AI agent?

Yes! Use the `update_incident` tool with the incident ID and your PagerDuty email. Set status to `acknowledged` or `resolved` to change the incident state instantly without opening the PagerDuty dashboard.

How do I find out who is on-call right now?

Run the `list_oncalls` tool. It returns every user currently on-call across all schedules and escalation levels, showing their name, escalation policy, and coverage window.

Can I create incidents programmatically for testing or escalation?

Absolutely. Use `create_incident` with your PagerDuty email, the target service ID, a descriptive title, and optionally set urgency to `high` or `low`. The incident will immediately trigger according to the service's escalation policy.

PagerDuty MCP Server for Pydantic AI 11 tools — connect in under 2 minutes

Name: PagerDuty
Availability: InStock
Author: Vinkius

ai.pydantic.dev/mcp

Built by Vinkius GDPR 11 Tools SDK

Pydantic AI brings type-safe agent development to Python with first-class MCP support. Connect PagerDuty through Vinkius and every tool is automatically validated against Pydantic schemas. catch errors at build time, not in production.

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

Open in ChatGPT Open in Claude Open in Perplexity

Vinkius supports streamable HTTP and SSE.

python

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 PagerDuty "
            "(11 tools)."
        ),
    )

    result = await agent.run(
        "What tools are available in PagerDuty?"
    )
    print(result.data)

asyncio.run(main())

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Stream every event to Splunk, Datadog, or your own webhook in real-time

* 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 PagerDuty MCP Server

Connect your PagerDuty account to any AI agent and take full control of incident management operations through natural conversation.

Pydantic AI validates every PagerDuty tool response against typed schemas, catching data inconsistencies at build time. Connect 11 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

Incident Management — List, create, acknowledge, and resolve incidents across all services
Service Monitoring — Browse all monitored services and inspect their configurations, integrations, and health status
User Management — List all team members, view individual profiles, contact methods, and notification rules
On-Call Visibility — See who is currently on-call across all schedules and escalation levels in real-time
Schedule Administration — Browse rotation schedules with their layers, handoff times, and coverage windows
Escalation Policies — Inspect escalation chains to understand how incidents route through teams

The PagerDuty MCP Server exposes 11 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.

How to Connect PagerDuty to Pydantic AI via MCP

Follow these steps to integrate the PagerDuty MCP Server with Pydantic AI.

Install Pydantic AI

Run pip install pydantic-ai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token

Run the agent

Save to agent.py and run: python agent.py

Explore tools

The agent discovers 11 tools from PagerDuty with type-safe schemas

Why Use Pydantic AI with the PagerDuty MCP Server

Pydantic AI provides unique advantages when paired with PagerDuty 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 PagerDuty integration code

Structured output guarantee: Pydantic AI ensures tool results conform to defined schemas, eliminating runtime type errors

Dependency injection system cleanly separates your PagerDuty connection logic from agent behavior for testable, maintainable code

PagerDuty + Pydantic AI Use Cases

Practical scenarios where Pydantic AI combined with the PagerDuty MCP Server delivers measurable value.

Type-safe data pipelines: query PagerDuty with guaranteed response schemas, feeding validated data into downstream processing

API orchestration: chain multiple PagerDuty tool calls with Pydantic validation at each step to ensure data integrity end-to-end

Production monitoring: build validated alert agents that query PagerDuty and output structured, schema-compliant notifications

Testing and QA: use Pydantic AI's dependency injection to mock PagerDuty responses and write comprehensive agent tests

PagerDuty MCP Tools for Pydantic AI (11)

These 11 tools become available when you connect PagerDuty to Pydantic AI via MCP:

create_incident

Requires the From header email (your PagerDuty user email), service ID, and incident title. Create a new incident on a service

get_incident

Get detailed information about a specific incident

get_service

Get detailed configuration of a specific service

get_user

Get detailed information about a specific user

list_escalation_policies

List all escalation policies

list_incidents

Optionally filter by status: triggered, acknowledged, resolved. List incidents across all services

list_oncalls

List who is currently on-call across all schedules

list_schedules

List all on-call schedules

list_services

List all monitored services

list_users

List all users in the PagerDuty account

update_incident

Use to acknowledge, resolve, or reassign incidents programatically. Update an incident status (acknowledge, resolve, escalate)

Example Prompts for PagerDuty in Pydantic AI

Ready-to-use prompts you can give your Pydantic AI agent to start working with PagerDuty immediately.

"Show me all triggered incidents right now."

"Who is on-call for the Platform team right now?"

"Acknowledge incident P8K2LMN and show me the service details."

Troubleshooting PagerDuty MCP Server with Pydantic AI

Common issues when connecting PagerDuty to Pydantic AI through the Vinkius, and how to resolve them.

MCPServerHTTP not found

Update: pip install --upgrade pydantic-ai

PagerDuty + Pydantic AI FAQ

Common questions about integrating PagerDuty MCP Server with Pydantic AI.

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 PagerDuty MCP integration works identically with OpenAI, Anthropic, Google, or any supported provider.