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Improvado's MCP server pulls PagerDuty incident data into your AI agent. Query open incidents, escalation history, MTTR trends, and on-call coverage — without logging into PagerDuty every time. Works with Claude, Cursor, and any MCP-compatible tool.
Stop clicking through incident timelines and service directories. Ask your AI agent for MTTR by team, recurring alerts by service, or on-call coverage gaps. The MCP server handles the PagerDuty API so you don't have to.
Your AI agent reads harmonized data across 500+ platforms. "Cost" in Google Ads and "spend" in Meta Ads resolve to the same field automatically.
Acknowledge incidents, add notes, reassign responders, and update escalation policies directly through your AI agent. Critical incident actions in one prompt — no more racing through the PagerDuty UI under pressure.
250+ governance rules enforce naming conventions, budget limits, and KPI thresholds. SOC 2 Type II certified.
Set up AI-powered watches on incident frequency, MTTR drift, and alert noise. Get notified about degrading reliability trends before they show up in post-mortems.
Automated weekly reports, anomaly flagging, and budget alerts — all from a single conversation. No more morning check-ins across 5 dashboards.
Acknowledge incidents, add notes, reassign responders, and update escalation policies directly through your AI agent. Critical incident actions in one prompt — no more racing through the PagerDuty UI under pressure.
Every phase runs through the same MCP connection. One protocol, all platforms, full governance. No switching between tools.
After a major incident, the timeline is split between PagerDuty alerts, Slack threads, and a Confluence doc someone partially filled in. Reconstructing the exact sequence of events — who was paged, when they acknowledged, what actions were taken — takes hours of manual archaeology.
Improvado's MCP server pulls the full incident timeline from PagerDuty: notification times, acknowledgment gaps, escalation steps, and resolution notes. Your AI agent assembles the chronology in seconds, ready to paste into a post-mortem template.
Services fire hundreds of low-priority alerts weekly. Engineers stop reading them. Then a real P1 gets buried in noise and goes unacknowledged for 40 minutes. You know you need to tune your alert policies, but identifying the noisy ones means exporting CSVs and doing the analysis yourself.
Ask your AI agent to analyze alert frequency by service and policy. It identifies which services generate high-volume low-priority noise versus which ones escalate to real incidents. Tune policies based on actual data, not guesswork.
Someone forgot to update the on-call schedule during a holiday week. A critical service has no primary responder assigned. Nobody notices until 2am when an incident fires and nobody gets paged. Auditing schedules manually across 20 services is tedious enough that it rarely happens.
Your AI agent audits PagerDuty schedules proactively. It surfaces gaps, services with a single point of failure on-call, and rotation overloads where one engineer is covering too many services simultaneously.
Same MCP connection, different workflows for every team member. Agency CEOs get portfolio health. Media Strategists get campaign QA. Analysts get cross-platform reports. Account Managers get auto-generated QBR decks. Creative Directors get performance-based briefs.
Each role asks in natural language. The MCP server handles the complexity — rate limits, auth, schema normalization, governance — behind the scenes.
Incidents (status, severity, timeline, responders, notes), services, escalation policies, on-call schedules, and alert frequency metrics. You can query across all services at once or drill into a specific team's incident history.
Yes. Acknowledge and resolve incidents, add notes, reassign responders, create maintenance windows, and update escalation policies — all through natural language. Write permissions are scoped to your PagerDuty API token.
Yes. The MCP server understands PagerDuty's structure — services, teams, escalation policies, and schedules. You can query by team, by service, or across the whole organization and the results respect your existing hierarchy.
PagerDuty's analytics require you to navigate their UI and work within their reporting templates. With Improvado MCP, you ask ad-hoc questions in plain English, combine PagerDuty data with data from other tools in the same query, and get instant answers without building reports.
Yes, the PagerDuty MCP integration is well-suited for post-incident analysis. An AI agent can retrieve full incident timelines, acknowledgment and resolution timestamps, escalation paths, and associated alert details to help reconstruct the sequence of events during an outage. You can ask questions like 'what was the mean time to resolution for P1 incidents in the last quarter' or 'which services had the most escalations last month' to surface patterns without manually exporting and analyzing PagerDuty reports.
Yes, the PagerDuty MCP integration can query on-call schedule data, escalation policy configurations, and team assignments through PagerDuty's Schedules and Oncalls API. This allows an AI agent to answer questions like 'who is on call for the payments service this weekend' or 'identify gaps in on-call coverage for next week' without navigating through multiple PagerDuty screens. This is useful for engineering managers and SRE leads planning rotations or auditing coverage before major deployments.
Connect your data to an AI agent in under 60 seconds. The closed loop starts with one conversation.
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