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Build a PagerDuty incident dashboard with Claude

Connect the PagerDuty MCP server to Claude, ask for an incidents and MTTR dashboard from your live on-call data, and publish it to a link your team comments on directly — no BI tool, no screenshots pasted into Slack.

What you'll build
A self-contained reliability dashboard — open incidents, MTTR and MTTA trends, incidents by service and severity, on-call load, and a recent-incident table — generated by Claude from your real PagerDuty data, then published to a drafty.im/canvas/… link. Your team clicks the exact chart or number they want changed and leaves a note. Claude reads the comments and ships a revised version to the same URL.

This is an end-to-end example: connect a data source over MCP, generate a dashboard from live numbers, and close the review loop on one link. Total time, start to shared link, is under fifteen minutes. The same shape works for any of the other examples — only the connection step changes.

Here's the finished dashboard, published to a canvas — click any tile or number to leave a comment, exactly as your team would:

Live canvas — comment on any elementOpen ↗

The three moving parts

  1. The PagerDuty MCP server gives Claude read access to your PagerDuty account — incidents, services, schedules, escalation policies, on-calls — through a controlled set of tools. You approve what it can touch.
  2. Claude pulls the numbers and writes a single self-contained HTML dashboard. You iterate on it in the artifact panel until it's right.
  3. Drafty turns that HTML into a stable link your team reviews. Comments pin to the exact element; Claude ships the fix to the same URL.

The generation step is fast now. The part this example is really about is the third one — getting the dashboard in front of people without losing their feedback to a screenshot circled in Preview.

Step 1 — Connect the PagerDuty MCP server

PagerDuty runs an official remote MCP server at https://mcp.pagerduty.com/mcp. You connect once; it authenticates over OAuth, so no token is pasted into a config file. (EU accounts use https://mcp.eu.pagerduty.com/mcp.)

In Claude Code:

claude
claude mcp add --transport http pagerduty https://mcp.pagerduty.com/mcp

Then run /mcp inside Claude Code and follow the OAuth prompt to authorize the account. The remote server is read-only out of the box — write tools are opt-in — which is exactly what a reporting dashboard needs.

In Claude Desktop: open Settings → Connectors → Add custom connector, paste https://mcp.pagerduty.com/mcp, and authorize with OAuth the same way.

Safety first
Authorize with OAuth and leave the server in its default read-only mode. If you'd rather run a local server or an unattended agent, the pagerduty-mcp package authenticates with a User API token scoped to read access — never enable write tools (--enable-write-tools) for a dashboard, and never commit a token into a repo. The dashboard only reads; it has no reason to acknowledge or resolve anything.

Step 2 — Pull the numbers

Ask Claude in plain language. It uses the MCP server's read tools to fetch real incident data:

claude
Using the PagerDuty MCP server, pull everything we need for a reliability dashboard: count of open incidents by urgency, incidents triggered in the last 30 days vs. the prior 30, mean time to acknowledge (MTTA) and mean time to resolve (MTTR) for the last 30 days, incident counts broken down by service and by severity, who is currently on call across our escalation policies, and the 10 most recent incidents with service, urgency, status, and resolve time. Summarize the figures before you build anything.

Claude calls PagerDuty, returns the figures, and you sanity-check them against the PagerDuty Analytics view before going further. This is the moment to catch a wrong assumption — a time window off by a day, low-urgency noise inflating the count, a service you forgot to scope out — while it's cheap.

Step 3 — Build the dashboard

Once the numbers look right, ask for the artifact:

claude
Build a single self-contained HTML dashboard from those figures. MTTR as the hero number with its 30-day trend, then tiles for open incidents by urgency, MTTA, and incidents this period vs. last. A bar chart of incidents by service, a small severity breakdown, an on-call panel, and a recent-incidents table at the bottom. Clean, no external dependencies — inline the CSS and any chart code.

Claude renders it live in the artifact panel. Iterate in place — you're not regenerating from scratch:

Step 4 — Publish to Drafty for review

A Claude artifact link is a preview, not a stable URL — iterate the artifact and the link you already sent now shows the old version. Ask Claude to publish it to a Drafty canvas instead, so the link you share always stays current:

claude
Publish this dashboard to Drafty as a canvas and give me the shareable link.

Claude pushes the dashboard and hands back a drafty.im/canvas/… link that renders on any device. Send it — your team opens it in a browser, no login and no Claude account needed.

Step 5 — The review loop

This is the part that's not obvious until you've done it once.

A reviewer clicks the specific tile, chart, or number they want changed and leaves a pinned comment — "this MTTR looks low, are we excluding the incidents that auto-resolved?" The comment is anchored to that element, not floating in a Slack thread. Claude reads the comments through the CLI, reruns the relevant PagerDuty query if needed, and pushes a revised dashboard to the same URL. The reviewer refreshes and sees the change; the thread stays attached to the element.

The mechanic matters because of what it removes. A Slack message about a chart produces "the number on the left looks wrong." A pinned comment on the actual tile produces "this — exclude auto-resolved incidents from the MTTR." One of those produces a correct revision; the other produces a guess.

Keeping it fresh

An MCP-generated dashboard is a snapshot — it holds the numbers Claude pulled when it built it; it doesn't re-query PagerDuty when someone opens the link. For a weekly reliability review or an incident retro, that's fine.

To make it a live canvas that always shows today's figures, copy this prompt — Claude sets up the refresh for you and schedules it to run on its own:

claude
Turn this PagerDuty dashboard into a live canvas: every morning, re-pull the latest incidents, MTTR/MTTA, and on-call data from PagerDuty via the MCP server, rebuild the dashboard, and push a new version to the same canvas URL so the link always shows today's figures. Schedule it to run daily on its own.

The link stays stable while the content updates underneath it — see keeping a canvas updated automatically.

What to watch for

PagerDuty dashboard with Claude — FAQ

Do I need to paste my PagerDuty API token anywhere?
No. The remote PagerDuty MCP server at mcp.pagerduty.com authenticates over OAuth, so you authorize the account through a consent screen instead of pasting a token. If you run the local pagerduty-mcp server or an unattended agent instead, it uses a User API token scoped to read access — never a write-enabled one, and never committed to a repo.
Is the dashboard live or a snapshot?
A snapshot. It contains the numbers Claude pulled when it built the file; it does not re-query PagerDuty when someone opens the link. To refresh it, ask Claude to repull and re-push to the same URL — or put that on a daily schedule so the stable link always shows current numbers.
Can my team comment without a PagerDuty or Claude account?
Yes. The dashboard is published to a Drafty canvas link that renders in any browser. Reviewers click the exact element they want changed and leave a pinned comment with no login required. Only the person connecting PagerDuty needs access to the account.
Is it safe to give Claude access to my PagerDuty account?
Connect with OAuth and leave the remote server in its default read-only mode — a reliability dashboard never needs more than that. Every tool call is mediated by the MCP server, and in Claude you approve actions. Don't enable write tools for a read-only reporting task.
How is this different from PagerDuty Analytics?
PagerDuty Analytics queries live data against the metrics PagerDuty maintains — the right choice for governed, standing reporting. This approach is for a fast, shareable snapshot you can spin up in minutes, shape exactly how you want by talking to Claude, then collect feedback on inline. Different jobs: one is a standing system, the other is a quick reviewable deliverable.