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 LangChain 11 tools — connect in under 2 minutes

Name: PagerDuty
Availability: InStock
Author: Vinkius

python.langchain.com/docs/integrations/tools/mcp

Built by Vinkius GDPR 11 Tools Framework

LangChain is the leading Python framework for composable LLM applications. Connect PagerDuty through Vinkius and LangChain agents can call every tool natively. combine them with retrievers, memory, and output parsers for sophisticated AI pipelines.

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 langchain_mcp_adapters.client import MultiServerMCPClient
from langchain_openai import ChatOpenAI
from langgraph.prebuilt import create_react_agent

async def main():
    # Your Vinkius token. get it at cloud.vinkius.com
    async with MultiServerMCPClient({
        "pagerduty": {
            "transport": "streamable_http",
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp",
        }
    }) as client:
        tools = client.get_tools()
        agent = create_react_agent(
            ChatOpenAI(model="gpt-4o"),
            tools,
        )
        response = await agent.ainvoke({
            "messages": [{
                "role": "user",
                "content": "Using PagerDuty, show me what tools are available.",
            }]
        })
        print(response["messages"][-1].content)

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.

LangChain's ecosystem of 500+ components combines seamlessly with PagerDuty through native MCP adapters. Connect 11 tools via Vinkius and use ReAct agents, Plan-and-Execute strategies, or custom agent architectures. with LangSmith tracing giving full visibility into every tool call, latency, and token cost.

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 LangChain 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 LangChain via MCP

Follow these steps to integrate the PagerDuty MCP Server with LangChain.

Install dependencies

Run pip install langchain langchain-mcp-adapters langgraph langchain-openai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token

Run the agent

Save the code and run python agent.py

Explore tools

The agent discovers 11 tools from PagerDuty via MCP

Why Use LangChain with the PagerDuty MCP Server

LangChain provides unique advantages when paired with PagerDuty through the Model Context Protocol.

The largest ecosystem of integrations, chains, and agents. combine PagerDuty MCP tools with 500+ LangChain components

Agent architecture supports ReAct, Plan-and-Execute, and custom strategies with full MCP tool access at every step

LangSmith tracing gives you complete visibility into tool calls, latencies, and token usage for production debugging

Memory and conversation persistence let agents maintain context across PagerDuty queries for multi-turn workflows

PagerDuty + LangChain Use Cases

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

RAG with live data: combine PagerDuty tool results with vector store retrievals for answers grounded in both real-time and historical data

Autonomous research agents: LangChain agents query PagerDuty, synthesize findings, and generate comprehensive research reports

Multi-tool orchestration: chain PagerDuty tools with web scrapers, databases, and calculators in a single agent run

Production monitoring: use LangSmith to trace every PagerDuty tool call, measure latency, and optimize your agent's performance

PagerDuty MCP Tools for LangChain (11)

These 11 tools become available when you connect PagerDuty to LangChain 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 LangChain

Ready-to-use prompts you can give your LangChain 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 LangChain

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

MultiServerMCPClient not found

Install: pip install langchain-mcp-adapters

PagerDuty + LangChain FAQ

Common questions about integrating PagerDuty MCP Server with LangChain.

How does LangChain connect to MCP servers?

Use langchain-mcp-adapters to create an MCP client. LangChain discovers all tools and wraps them as native LangChain tools compatible with any agent type.

Which LangChain agent types work with MCP?

All agent types including ReAct, OpenAI Functions, and custom agents work with MCP tools. The tools appear as standard LangChain tools after the adapter wraps them.