Workflow n8n

Traffic Spike Reporter

An n8n workflow built for agencies running multiple client properties. Every morning it pulls GA4 and Search Console data from Databox for each configured client, runs parallel AI root cause analysis per property, and posts individual anomaly reports plus a consolidated operations summary to Slack - before clients notice anything is off.

~5 min setup
Runs on demand
n8n + Databox MCP

Get it free →

See example output

What it does

Built for agencies managing multiple clients, this workflow runs every morning on a schedule. For each configured client, it pulls four metrics from Databox via MCP – sessions and new users from GA4, ad cost and clicks from Google Search Console – comparing yesterday against the prior day. Two AI agents then run in parallel per client: one investigates the traffic data, the other cross-references paid and organic signals to produce a root cause hypothesis. Individual client reports are aggregated into a single operations summary and posted to Slack. Client configuration is handled in a single code node – add a client name, GA4 data source ID, GSC data source ID, and metric keys. Databox Genie can supply the correct IDs and keys on request. Designed for daily use by agency ops teams or growth leads managing more than one property.

Who it's for

Heads of growth and operators
You run the numbers on Monday. This replaces the 20 minutes of tab-switching with a brief that is ready before your standup.
Agency analysts
Run it for each client. Get a consistent read on every account in the time it used to take to review one.
Founders doing their own analytics
You know what your metrics are. This tells you what they mean and what to do about it.

What's in the package

Security scanned Reviewed by Databox

traffic-spike-reporter-setup-guide.pdf MCP connection steps, configuration options, example run
traffic-spike-reporter.json

What you need to run this

n8n

Free and open-source. Self-host it or run it on n8n Cloud to import and execute the workflow.

Databox account

The workflow reads your live metrics through Databox MCP. The free plan includes the integrations you need to get started.

MCP connection

Connector inside your n8n. Takes less than a minute to set up. Full instructions in the setup guide included in the download.

Don't have Databox yet?

Free to start. Connect your first integration in minutes — no credit card required.

Start free with Databox →

How to use it

Download the workflow
Click Get it free above. The workflow file downloads to your machine.
Import into n8n
Open n8n → Workflows → Import from File, and select the downloaded JSON.
Connect Databox
Connecting your Databox account via the MCP node is a quick process that takes less than a minute. You'll need a free Databox account to do this.
Schedule and activate
Set the schedule or trigger, then activate. The workflow runs against your live Databox data automatically.

Connect any AI tool to your Databox data with Databox MCP.

Databox MCP is the bridge between your live metrics and any AI — Claude, ChatGPT, Cursor, or any client that speaks MCP. One auth, every workspace, no scraping.

Learn more

Databox MCP hub connected to OpenAI, Claude, Cursor and n8n — one MCP integration links every AI tool to your live data

Built on Databox

No AI hallucinations. Analysis built on a real context layer.

Ask a generic AI to analyse your business performance and it will give you a confident answer. It will also be working from assumptions.

Generic AI doesn't know how your business defines a qualified lead, what your MRR calculation includes, or how you attribute revenue. It fills those gaps with the most plausible interpretation it can find — which is different from the correct one.

The products here run on Databox's data layer — if you choose so. Your metric definitions, your reporting logic, and your live numbers are what the AI reads, pulled straight through Databox MCP instead of uploaded by hand. That's the difference between an output you can share with your team and one you have to verify before you trust it.

Example output

Common questions

Do I need a Databox account to use this skill?

Yes, ideally — and here's why it matters. Point a generic AI at a raw data export and it has to guess what your numbers mean: which conversions count, how you define an engaged session, what a "normal" week looks like. It fills those gaps with the most plausible interpretation it can find, which is often not the correct one. The skill avoids that by reading your data live from Databox through MCP, where your metrics are already standardized — one consistent definition for sessions, engagement, and conversions across your setup. The AI reads what's actually true for your business instead of inferring it, so the report is one you can trust rather than verify. You can technically run it against data you've pasted in by hand, but you lose that context layer and the output is only as reliable as the export. If you don't have an account or data source connected yet, the skill walks you through the setup.

Can I use this with the free Databox plan?

Yes — the free Databox plan includes the integration this skill needs, so you can run it at no cost. The skill itself is also free to download.

Does this use live data from my connected source?

Yes — every run reads your live data directly from Databox through the MCP connection. The report reflects your current data at the moment you run it, not a cached or uploaded snapshot.

Do I need to export data manually?

No — there are no CSV exports and no copy-paste. The skill pulls your data straight from Databox through MCP each time it runs, so the report is always built from your live source.

Can agencies run this for multiple clients?

Yes — run it against each client's data connected in Databox, one account at a time, and you get the same structured weekly brief for every client. It's built to give a consistent report you can take to each client review.

Can I customize the report?

Yes. Before it runs, the skill asks a few onboarding questions — what you want to measure, the time frame, and any context that matters for the analysis — so the report is shaped to your goals from the start. After a run, you can give it feedback to adjust focus, sections, or anomaly thresholds, and reinstall the updated version so your changes carry into future runs.