Build a GitLab pipeline & MR dashboard with Claude

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:

The three moving parts

1. The GitLab MCP server gives Claude read access to your GitLab data — merge requests, pipelines, jobs, issues, project search — 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 GitLab MCP server

GitLab runs an official MCP server at https://gitlab.com/api/v4/mcp (for self-managed instances, swap in your own host). You connect once; it authenticates over OAuth 2.0, so no token is pasted into a config file.

In Claude Code:

Then run /mcp inside Claude Code and follow the OAuth prompt to authorize the account. When you authorize, grant read scopes only — this dashboard never needs to write to GitLab.

In Claude Desktop: open Settings → Connectors → Add custom connector, paste https://gitlab.com/api/v4/mcp, and authorize with OAuth the same way.

Step 2 — Pull the numbers

Ask Claude in plain language. It uses the MCP server's read tools (get_merge_request, get_merge_request_pipelines, get_pipeline_jobs, search) to fetch real data:

Claude calls GitLab, returns the figures, and you sanity-check them against the GitLab UI before going further. This is the moment to catch a wrong assumption — the wrong default branch, draft MRs counted as open, a pipeline status you didn't expect — while it's cheap.

Step 3 — Build the dashboard

Once the numbers look right, ask for the artifact:

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

• "Make pass rate the biggest number and put pipeline duration right under it."
• "Add a 6-week pass-rate trend line."
• "Sort the open MRs by age, oldest first, and flag anything over 7 days."

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 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 pass rate looks high, are we counting only the default branch?" The comment is anchored to that element, not floating in a Slack thread. Claude reads the comments through the CLI, reruns the relevant GitLab 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 scheduled-pipeline runs from the pass rate." 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 GitLab when someone opens the link. For a weekly review or a sprint-review snapshot, 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:

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

What to watch for

• Read-only, always. An engineering dashboard needs read access and nothing more. Scope the OAuth grant or token to read_api.
• Check the figures before you share. The MCP returns exactly what you ask for — if your "pass rate" query includes manual or scheduled pipelines you didn't mean to count, or your "open MRs" includes drafts, the dashboard will confidently show the wrong number. Reconcile against the GitLab UI once.
• The link is the deliverable, not the artifact. Share the Drafty URL, not the Claude artifact preview — that's the one you can update in place.