How to Run Your Omnisend Account From Claude With MCP

Most AI tools can help you write an email.

That is not particularly interesting anymore.

The more useful shift is giving AI access to the actual email marketing account, so it can see the subscribers, campaigns, automations and performance data you would normally have to dig through manually.

That is what Omnisend's MCP integration makes possible.

By connecting Omnisend to Claude, you can ask questions about the account in plain English, analyze what is happening and, in some cases, have Claude take the next action for you.

By Emiel Dingemans, Founder of Magicianly

This video was created in partnership with Omnisend.

In the video above, I used the integration to:

• check the health of an Omnisend account;
• analyze deliverability signals;
• identify valuable audience segments;
• create a segment directly inside Omnisend;
• analyze previous campaign performance;
• draft and build a re-engagement campaign.

I never needed to manually work through the Omnisend dashboard for those tasks.

Here is how it works and where I think it is genuinely useful.

What Is the Omnisend MCP?

MCP stands for Model Context Protocol.

In simple terms, it creates a connection between an AI assistant and another platform.

Without that connection, using AI for email marketing normally looks something like this:

1. 1Open your email platform.
2. 2Export data.
3. 3Copy the relevant information into an AI chat.
4. 4Ask the AI to analyze it.
5. 5Take the answer.
6. 6Go back into the email platform.
7. 7Make the change yourself.

The AI can help you think, but it cannot actually see the account unless you manually give it the information.

With Omnisend MCP connected, Claude can work with the live account context instead.

That means you can ask questions about things such as:

• contacts;
• subscriber growth;
• campaigns;
• automations;
• revenue;
• engagement;
• segments;
• products;
• deliverability signals.

More importantly, the connection is not limited to reading information.

Depending on the permissions you grant it, the AI can also perform actions inside Omnisend, such as creating segments and building campaign drafts.

That is where MCP becomes much more useful than another AI copywriting button inside an ESP.

How to Connect Omnisend to Claude

Connecting the account is relatively straightforward.

Inside Claude:

1. 1Open Settings.
2. 2Go to Customize.
3. 3Open Connectors.
4. 4Browse the available connectors.
5. 5Search for Omnisend.
6. 6Connect the Omnisend MCP.
7. 7Authorize access to the correct Omnisend account.

Once connected, start a new Claude conversation and make sure the Omnisend connector is enabled for that chat.

A simple test prompt is:

Give me a snapshot of my Omnisend account. How many contacts do I have? What's my subscriber growth trend and when was my last campaign sent?

If everything is connected correctly, Claude should retrieve the information directly from the Omnisend account.

I would recommend using a reasoning-capable model for more complicated analysis rather than choosing the fastest possible model.

The more complicated the question becomes, the more important it is that the model can reason across several pieces of account data rather than simply retrieve one number.

Use Case 1: Run a Weekly Deliverability Health Check

One of the simplest useful applications is monitoring email deliverability.

You could manually review campaign after campaign and compare:

• bounce rates;
• spam complaints;
• unsubscribe rates;
• opens;
• clicks;
• unusual changes between sends.

Or you can ask the AI to review those signals together.

Are my deliverability signals healthy right now? Check the bounce rates, spam complaints and unsubscribes across my recent campaigns and flag anything concerning.

In the demo account, Claude pulled the recent campaign data, summarized the overall situation and highlighted the unsubscribe rate as the signal worth investigating.

It also noticed data that did not look realistic.

Because the video uses a test account, several campaigns showed behavior that would be unusual in a real ecommerce account. Claude called that out rather than blindly treating every number as trustworthy.

That matters.

The value is not simply getting a dashboard summary.

The useful part is asking:

• What looks abnormal?
• What changed?
• What should I investigate next?

For an ecommerce operator, this could easily become a weekly two-minute check instead of another reporting task.

Use Case 2: Find Better Segments From Your Existing Data

The second use case is audience discovery.

Instead of starting with a list of generic ecommerce segments, you can ask Claude to look at the actual account and identify opportunities based on:

• contact data;
• purchase history;
• engagement;
• campaign performance;
• automations;
• product data.

For example:

Analyze my contact list, purchase history and engagement data and recommend three high-impact segments I should create to improve revenue. Explain why each one matters.

In the demo, Claude identified three useful opportunities.

1. New Subscribers Who Have Not Purchased

The account had a growing group of subscribers who had not yet placed their first order.

More importantly, Claude noticed there was no active Welcome Series serving that audience.

That makes the segment useful because it exposes a broader lifecycle gap rather than simply producing another audience to send campaigns to.

2. One-Time Buyers Ready for a Second Purchase

Claude also looked at the product catalogue and purchase behavior.

The interesting part was that it did not simply say:

Send a winback discount.

It tried to understand what a logical second purchase might be based on the products customers were already buying.

That is much closer to how segmentation should actually work.

3. Engaged 90-Day Subscribers

The third recommendation was a recent-engagement audience based on people who had opened or clicked within the previous 90 days.

That can become a useful default campaign audience and also helps reduce unnecessary sending to disengaged subscribers.

Claude Can Build the Segment Too

This is where the integration becomes more interesting.

After Claude recommended the engaged 90-day audience, I simply asked it to create the segment.

It did.

When I opened Omnisend afterward, the new segment was already there with the conditions applied.

That removes one of the least interesting parts of email marketing work.

You can spend your time deciding:

• which audience matters;
• why it matters;
• what you should say to them;

instead of spending that time clicking through segment builders.

The AI is useful here because it moves from analysis to execution.

Use Case 3: Find an Audience and Build a Campaign for It

The final example combines everything.

Instead of only asking Claude to analyze the account, I asked it to:

1. 1identify a specific group of contacts;
2. 2create the segment;
3. 3analyze previous campaign performance;
4. 4recommend a subject line;
5. 5write the campaign;
6. 6save it as a draft inside Omnisend.

The prompt was:

Find contacts who haven't purchased in 90+ days but opened at least two emails in the last 30 days. Create a segment for them, then draft a re-engagement campaign with a compelling subject line based on what's worked in my recent sends.

Claude found the contacts and created the segment.

But it also caught an important issue in my prompt.

Someone who has not purchased in the last 90 days is not automatically a previous customer.

The audience could also contain people who have never purchased.

Claude pointed that out and suggested adding a condition requiring at least one historical order if the goal was a true customer winback campaign.

That is exactly the kind of reasoning you want from an AI assistant.

Not blindly doing what you ask, but identifying when the logic behind the request may not match the intended outcome.

Using Previous Campaign Data to Write the Email

Claude then looked at the subject lines and campaign performance already inside the account.

Instead of generating a subject line in isolation, it tried to identify patterns from previous sends.

It then created:

• a subject line;
• alternative subject lines;
• a preheader;
• campaign body copy;
• product recommendations;
• a CTA.

It even used the account's product pricing and average order data when reasoning about the copy.

Once approved, I asked Claude to build and save the campaign as a draft.

The campaign then appeared inside Omnisend.

Would I immediately send the AI-generated email without reviewing it?

No.

The draft was useful, but our team would still improve the strategy, copy and design before putting it in front of customers.

That distinction matters.

What AI Still Cannot Do for You

Connecting an AI assistant to your email platform does not mean the AI suddenly understands your entire business.

It does not automatically know:

• your margins;
• your internal priorities;
• how aggressively you want to discount;
• your full brand voice;
• your merchandising strategy;
• what customers are telling your support team;
• which products you are trying to grow;
• whether an attributed sale was genuinely incremental.

It can only reason from the context and data available to it.

For example, an AI might recommend a discount because it predicts that the campaign will convert.

That does not mean discounting is the right business decision.

Maybe you spent the last two years teaching customers not to wait for promotions.

Maybe your margins cannot support the offer.

Maybe the audience is already engaged enough that a discount is unnecessary.

Those decisions still require judgment.

Where I Think Omnisend MCP Is Most Useful

I would not think about this as:

AI can now run my entire email marketing program.

I would think about it as removing a large amount of the work that happens before the important decision.

MCP is particularly useful for:

• pulling account data;
• recurring health checks;
• summarizing performance;
• finding unusual changes;
• discovering audience opportunities;
• creating segments;
• researching previous campaign performance;
• building first campaign drafts;
• turning an analysis into an actual action.

Those jobs involve a lot of clicking, filtering and moving between screens.

AI can now do much more of that work inside one conversation.

The strategic question is still yours:

What should we actually do with the information?

Claude vs ChatGPT for Omnisend MCP

The video uses Claude, because that is the AI workspace we have been using heavily inside Magicianly.

But Omnisend's MCP can also connect with ChatGPT.

The underlying idea is the same:

Give your AI assistant secure access to the Omnisend account, then use natural-language prompts to analyze the account and perform supported actions.

The tool matters less than the workflow.

The biggest improvement is no longer having to constantly move data between the ESP and the AI assistant manually.

The Bigger Shift: AI Is Moving From Advice to Execution

For the last few years, most ecommerce AI tools have essentially been generators.

• Write me a subject line.
• Rewrite this paragraph.
• Give me five campaign ideas.

Useful, but limited.

MCP changes the relationship.

The AI can now see what is happening inside the account, reason about the data and take certain actions based on the conversation.

That makes the workflow much closer to:

• Analyze this.
• Tell me what matters.
• Now do it.

There still needs to be someone making the right decisions.

But the amount of manual work between the question and the execution is shrinking quickly.

For busy ecommerce teams, that is the part I find most interesting.