I Put Model Context Protocol to the Test in My Marketing Stack
Amidst all the usual AI noise, you may have heard the letters MCP. Maybe it passed in one ear and out the other, but it's worth pausing on this acronym for a minute because it represents the potential of significantly simplifying the way we work.
But let's back up and start with a definition. What, exactly, is MCP?
Instead of explaining everything to the AI each time, it can look at real data in your CRM, email, project management tools, and other software to give you informed answers and insights – then have a conversation with it to get even more detail and insight.
It’s like a tiny research assistant running around your various databases and connecting dots.
MCP is fundamentally different because it's conversational and dynamic. Instead of pre-programming specific actions, you're giving AI the ability to understand and interact with your tools in real-time based on what you actually need right now.
Traditional integrations are like having a really efficient assembly line - great if you know exactly what you want to build, every time. MCP is more like having a Swiss Army knife that can adapt to whatever problem you're facing right now.
Like you, I’ve heard all the MCP hype and recently had a chance to try it out for myself. What I found out is that, as with a lot of things in AI, the reality and the potential are not yet quite the same.
The Promise: AI can connect to your project management tools to analyze workflows and make improvements and updates directly.
What I Tested: We're currently running an AI transformation project for a media buying agency, using Claude as our information repository for goals, plans, and timelines. When we learned about the Asana MCP connector, we decided to test whether Claude could improve our actual project management workflow.
What We Did: We connected Claude to our Asana project board and asked it to scan the tasks and, based on what it already knew about our project goals, suggest ways to better describe and align tasks to make them clearer for team members.
What Worked:
What Surprised Me: This was the first MCP integration I tested that could actually take action, not just provide analysis. The integration was seamless – Claude understood both our project context and how to make real improvements in the tool itself, making it feel more like a true AI collaborator.
The Reality Check: While this worked well for task optimization and project organization, it's still limited to the specific capabilities Asana exposes through the integration.
What I Tested: I asked Claude to analyze my HubSpot database and identify the most engaged contacts based on comprehensive engagement metrics like email opens, clicks, form submissions, deal activity, and sales interactions.
What Worked:
What Surprised Me: The depth of analysis was remarkable. Claude didn't just pull basic data – it created engagement categories, calculated composite scores, identified patterns across different types of activities, and even provided strategic recommendations based on the findings. It turned raw CRM data into genuine business intelligence.
The Reality Check: This is still read-only access. Claude can provide incredibly sophisticated analysis of your HubSpot data, but you still need to go into HubSpot to actually follow up with contacts or take action. However, the analytical capabilities are genuinely impressive and would have taken hours to replicate manually.
What I Tested: I asked Claude to create the content of the proposal, then applying my existing brand guidelines and matching the style of previous work to create an all-new proposal.
What I Expected: Based on my back-and-forth with Claude, I thought it would be able to create new designs by analyzing my existing brand assets and applying those standards to new content.
The Reality: This was the biggest disappointment. The Canva integration is currently read-only. Claude can analyze your existing designs, extract text content, and understand your design patterns, but it can't actually create new designs or apply brand standards automatically. Claude wrote the content of the proposal for me but was unable to take it across the finish line.
After testing these integrations, here's my honest assessment:
The Good News
MCP works best for intelligence and analysis. If you need to understand patterns across your data, ask complex questions about your business, or get insights that would normally require pulling reports from multiple tools, it's genuinely useful.
The conversational interface is powerful. Being able to ask follow-up questions and dig deeper into data without switching tools creates a completely different workflow. It's like having a data analyst who never gets tired of your questions.
Setup is surprisingly easy. Connecting these tools took minutes, not the weeks of developer time that traditional integrations often require.
The Reality Check
Most integrations are still read-only. You can analyze and understand your data better, but for the most part you still need to go to the original tools to take action.
Creation capabilities are limited. Despite the marketing promises, Claude can't automatically generate new designs, create complex documents, or execute sophisticated workflows across multiple tools.
The current reality doesn't match the marketing hype yet, but the direction is promising. For marketers willing to experiment, MCP integrations offer genuine value for data analysis and business intelligence, even if the full automation promises are still on the horizon.
The technology feels like we're at the "iPhone 3G" moment – clearly the future, obviously transformative, but not quite ready to replace everything we do today.
And honestly? That might be exactly where we want to be. The last thing marketers need is another overhyped tool that promises everything and delivers disappointment. Better to have realistic expectations and be pleasantly surprised as capabilities improve.
If you're curious about testing MCP yourself, start with one integration (HubSpot is probably the most mature), set realistic expectations about what it can do today, and treat it as an experiment in how you might work differently in the future.
The future of AI-powered marketing is coming. It's just taking a more practical path than the demos suggested.
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