Everyone who has used AI for PPC has heard the same advice: give it more context.

But giving more context means you export a campaign report, download keyword data, pull the ad performance sheet, upload all of it, and write out enough background for the AI to understand what it’s looking at.

Then at some point the chat gets heavy, responses get sluggish, and the easiest fix is to open a fresh one. That means re-uploading everything, re-writing the context, and starting over from scratch.

So we built Optmyzr MCP to move that work off your plate. Instead of exporting files and re-building context every time you open a new chat, Claude connects directly to Optmyzr and pulls what it needs as the conversation moves forward.

Here are five things you can do from one conversation, without exporting a file or repeating yourself.

1. Run a MoM account audit in one prompt

Doing a PPC audit with Claude means a lot of prep work before you even type a prompt.

Export campaign reports, pull keyword data, download ad performance, upload all of it, and then get started. We wanted to see if one prompt could replace all of that.

Claude made eight live calls to Optmyzr, pulled account and campaign data across four months, and returned a full analysis in minutes.

No exports or spreadsheets, in fact, I didn’t even have Optmyzr open.

What made it useful

1. The analysis came back with compelling visuals for easy understanding.

Claude didn’t return a block of text with numbers buried in paragraphs.

It came back with metric cards for each month, trend charts for ROAS, conversions, spend and Search IS, a colour-coded breakdown of what was improving, what wasn’t, and a verdict.

The structure made it easy to scan and even easier to act on.

2. It didn’t just flag January, it explained it.

Rather than surfacing 915% ROAS as a low number and moving on, Claude traced it back to a single paused Display remarketing campaign spending €1,101 with zero conversions, and showed how much that one campaign was pulling the account-level numbers down.

3. It connected two problems that might look unrelated in a standard report.

The budget was going to two Shopping campaigns with zero tracked conversions every month and Search IS sitting under 50% every month.

Claude linked them directly and named the campaigns involved.

These two findings became the basis for the Rule Engine strategy in the next section.

4. It flagged something that doesn’t appear as a metric anywhere.

A campaign with a Paused status showing up as the second-highest spender in the account every single month. Claude pointed to it as something worth investigating and explained why it was structurally unusual.

2. Conduct a cross-account analysis

Running a cross-account analysis with a generic AI tool means one export per account, each uploaded separately, with enough written context for the AI to understand what it’s looking at.

Do that across seven accounts and you’ve done seven versions of the same work before the analysis has even started.

We wanted to see what one prompt could do connected to Optmyzr MCP.

Claude pulled account-level data across all five and returned this.

*One account in this analysis uses real data pulled live via Optmyzr MCP. The remaining four are illustrative but accurately represent the structure and depth of what Claude returns.*

Every account came back with a status, a spend trend, and a reason for its ranking.

The accounts flagged as critical and urgent weren’t ranked that way because of spend levels, but because of patterns- zero conversions across four months of consistent CTR, and a ROAS in freefall while spend kept climbing.

Those are different problems with different fixes, and Claude named both separately.

The patterns section at the bottom is what makes this different from looking at accounts one at a time.

Budget allocation being inverted across the portfolio, three accounts constrained by impression share for the same underlying reason, seasonal uplift hitting multiple accounts in the same window, none of that is visible when you review accounts individually.

It only shows up when you put them side by side.

Because it’s a live conversation, we kept going after the initial output:

“The account ranked number one for urgent attention, what’s the most likely root cause and what are the first two things I should check?”

Claude went deeper without needing any context repeated.

That’s the part that’s harder to replicate with exported files. When the analysis raises a new question, you can just ask it and the conversation continues from where it left off.

3. Audit your account alerts and fill the gaps

A campaign that wasn’t a risk a year ago might be your biggest one today, but if nobody updated the alerts, nothing is watching for it.

With a generic AI tool, auditing your alerts means exporting alert configurations and account performance data separately, then asking the AI to cross-reference them.

Creating new alerts based on that analysis means going back into the platform yourself.

Claude pulled all eight configured alerts along with the most recently triggered ones, then pulled live campaign performance data to cross-reference both.

The existing setup wasn’t the problem. Conversion volume at account level, cost monitors on key campaigns, impression share alerts on the top Shopping campaigns. One alert had already fired, flagging spend running 68% over target on a Search campaign.

The alerts were doing what they were supposed to do.

What they weren’t covering was two gaps that mattered specifically for this account.

• Conversion volume was being tracked but not cost per conversion
• The account’s best-performing Search campaign had no impression share alert

If that share dropped further, nothing would flag it.

Claude explained why each gap mattered given what it had just seen in the performance data, then created both alerts after asking for confirmation.

So, reading the existing alerts, cross-referencing with live account data, identifying what was missing, and creating the new ones, all of it happened inside one conversation.

4. Generate a Rule Engine strategy in minutes

Rule Engine lets you set up conditional if-this-then-that rules that run against your account on a schedule. Building one usually means starting from a pre-built template or constructing conditions manually.

We wanted to see if Claude could skip that entirely.

The audit had already identified the problems. Could Claude now build the rules to fix them, without being told what to create?

Claude had the full context from the audit earlier in the conversation and used it to decide what to build. It went back to the two problems the audit had already named and proposed a rule for each.

What made it useful

The strategy wasn’t generic. Claude didn’t suggest rules that could apply to any account, it built specifically around what the audit had flagged in this one.

Rule 1 targets the zero-conversion budget drain directly.

Rather than pausing campaigns outright, it reduces bids by 30%, a softer intervention that slows the waste without cutting the campaign entirely.

Rule 2 targets the impression share gap.

The audit found Search IS stuck under 50% while the best-performing campaign was being starved of budget. The rule finds enabled campaigns running above 800% ROAS but below 55% IS and increases their daily budget by 20%.

Every campaign touched by either rule gets labelled automatically so you can track what changed and when. As you can see, the whole thing happened in the same conversation as the audit.

Claude used the context it already had rather than starting from scratch.

5. Review account optimization recommendations

If you’re pulling optimization recommendations with a generic AI tool, you’re back to the same problem. We wanted to see what it looked like when Claude could read the recommendations directly from Optmyzr, with the full account context already in the conversation.

So, here’s what we did:

Claude pulled four recommendation categories and ranked them by urgency.

Even before ranking these it pointed out the most urgent recommendation:

Several of the account’s best performing ad groups were hitting their ROAS targets.

But what Claude caught was that the targets were set so high that Google couldn’t bid competitively enough to win most eligible auctions.

Some ad groups were losing up to 95% of impressions to rank because the targets were too tight to let Google compete. Claude flagged it, explained it, and pulled the exact recommended ROAS adjustment for each affected ad group.

Then it gave you the direct link to go apply it in Optmyzr.

How to set it up yourself

Setting up Optmyzr MCP takes just a few minutes. Here’s the quick version:

Step 1: Generate your MCP API key

Go to Optmyzr Settings → MCP Integration and create your API key. Store it securely (you’ll only see it once).

Step 2: Connect your AI assistant

Add Optmyzr MCP to your preferred AI tool (like Claude or ChatGPT or any MCP-supportive AI tool) using the MCP endpoint and your API key.

Step 3: Start using live PPC data in AI

Once connected, your AI assistant can pull reports, run analysis, and build workflows directly from your Optmyzr account.

For full setup instructions, refer to the official MCP setup guide here!

Connect your AI assistant directly to Optmyzr

With Optmyzr MCP, you no longer have to spend time extracting data, building reports, and then trying to make sense of it all in a separate tool. Instead, start asking the right questions with live PPC context already embedded in your favorite AI tool.

Ready to ditch the back-and-forth?

Start your 14-day fully-functional free trial and connect Optmyzr MCP to your AI assistant today!