Today, we’re announcing that PagerDuty is now available in AWS QuickSuite through the Model Context Protocol (MCP). This means PagerDuty’s incident management capabilities can now connect with the 1,000+ applications and data sources that QuickSuite integrates with, from AWS services to enterprise SaaS platforms, all accessible through natural language.

Access PagerDuty Data from QuickSuite

If you use AWS QuickSuite, you can now opt in to access PagerDuty’s incident data, schedules, and services directly within QuickSuite’s AI interface. And, you can combine PagerDuty with other connected applications in ways that weren’t previously possible without custom integration work for deeper operational context.

Here are 3 examples that show you can make better use of PagerDuty data via QuickSuite:

1. Query PagerDuty incident data alongside information from other business systems in a single, natural language request. Ask “Which customers are affected by today’s incidents?” and get PagerDuty incidents matched with customer accounts from your CRM in one answer.
2. Build automated workflows that trigger PagerDuty actions based on events in other applications. When a deployment fails in your CI/CD platform, automatically create a PagerDuty incident and page the relevant team.
3. Include PagerDuty operational context when using QuickSuite’s research and analytics capabilities. Generate executive reports that combine PagerDuty’s MTTR metrics with business impact data from customer service platforms.

AI-powered Incident Management Use Cases

Whether you’re investigating a single outage or analyzing patterns across months of incidents, PagerDuty in QuickSuite gives you cross-platform intelligence. These two examples show the range: using PagerDuty as your operational hub during active incidents, and leveraging AI to uncover insights from your incident history.

PagerDuty as Your Operational Hub

Ask QuickSuite “What’s the full impact of the payment service outage?” and it starts with PagerDuty, pulling the incident timeline, affected services, and responders. This operational context tells QuickSuite exactly what to investigate: QuickSuite can then be tasked with  querying observability tools for metrics on those specific services, checking existing ITSM platforms for recent changes, and searching business collaboration tools for customer communications. The result is a complete impact analysis driven by PagerDuty’s operational data, which can then be captured back in the incident record so your operational knowledge compounds. Each incident builds context for the next.

Pattern Recognition Across Incident History

Using Quick Research, teams can ask questions like “Why do our database incidents spike after deployments on Fridays?” The AI analyzes months of PagerDuty incident data, correlates it with deployment timing from DevOps platforms, examines code changes that preceded incidents, reviews customer impact from support systems, and identifies configuration patterns that predict failures. It delivers a research report with specific recommendations and tailored  analysis of causation that would take an SRE team days to manually investigate.

Getting Started

PagerDuty’s MCP Server is available now in AWS QuickSuite. If you’re already using both PagerDuty and QuickSuite, you can connect them through QuickSuite’s Actions integrations.

PagerDuty is committed to allowing our users to work from where they want, whether that’s our UI, QuickSuite or Q, or other AI platforms. See how our MCP server is helping customers like Block manage incidents in the AI era in this blog post. Or watch this MCP demo.

If you want to learn more about how PagerDuty is making incident management accessible across AI platforms through MCP here. You can use PagerDuty’s MCP Server from our GitHub repository or our remote server with other MCP-compatible tools.