Mastering MCP: Critical Challenges and Key Considerations for Marketers

The Model Context Protocol (MCP) is changing how we think about AI integration in marketing. But like any emerging technology, it comes with its fair share of challenges. If you're a marketer considering MCP, here's what you need to know about the roadblocks and considerations ahead.

The cloud hosting gap

Here's the thing that'll surprise you: despite all the buzz around MCP and the growing number of tools available, there's still no official cloud-hosted option for many essential marketing tools. Take Google Analytics 4, for example. You'd think something this fundamental would have seamless MCP integration by now, but nope.

This means you're often stuck setting up your own infrastructure or waiting for third-party solutions that may or may not meet your security standards.

No industry standard yet

We're still in the Wild West phase. There's no universally accepted standard for how MCP should work across different platforms and tools. This creates a headache when you're trying to build consistent workflows across your marketing stack.

Everyone's doing their own thing, which means more complexity for you as you try to piece together solutions from different vendors.

The speed of change

MCP is evolving fast. Really fast. What works today might be outdated in three months. This rapid pace makes it tough to commit to long-term implementations or training programmes. You're essentially building on shifting sand.

The context problem

Most MCP interactions happen through inference, which sounds fancy but creates real problems. You need to provide massive amounts of upfront context to get useful results. And here's the kicker: every time you use a tool, it eats up even more context than if you just wrote and ran the code yourself.

This context hunger becomes expensive and slow. You're constantly feeding the system background information about your campaigns, customer segments, and business goals. This article dives deeper into context management strategies that can help.

Documentation nightmares

Not all APIs play nice with MCP. Sure, you can take OpenAPI specifications and automatically generate MCP tools, but the reality is messier. Many APIs are poorly documented, missing the high-level examples that make LLMs actually useful.

The difference is night and day. When OpenAPI schemas include real examples, LLMs perform significantly better. Remove those examples, and you're back to guessing what inputs actually work.

The discovery problem

Here's where things get really messy. Current MCP tool discovery is pretty basic – just names and descriptions. This works fine when you have a handful of tools, but scale up to hundreds of tools or deal with complex capabilities, and the system breaks down.

You hit a double scaling challenge:

• Horizontal scaling: Too many tools to sort through efficiently

• Vertical scaling: Individual tools too complex to explain in a brief description

Token budget constraints make this worse. You're forced to choose between having access to lots of tools or having detailed explanations of what each tool actually does. The flat structure of tool listings doesn't help either – LLMs struggle to navigate large ecosystems or understand sophisticated tools with multiple capabilities.

What this means for marketers

Don't let these challenges scare you off MCP entirely. But do go in with realistic expectations. Start small, expect some frustration, and budget extra time for setup and maintenance.

The technology is promising, but we're still in the early adopter phase. If you're risk-averse or working with tight deadlines, you might want to wait for more mature solutions.

For those ready to experiment, focus on simple use cases first. Build your context management strategy early, and don't be afraid to fall back to traditional methods when MCP isn't delivering.

The future of AI-powered marketing tools is bright, but we're not quite there yet. Understanding these challenges upfront will save you headaches down the road.

"We are on-track to deliver a 43% increase in inbound leads this year. There is no doubt the adoption of Growth Method is the primary driver behind these results."

Laura Perrott, Colt Technology Services

Growth Method is the GrowthOS built for marketing teams focused on pipeline — not projects. Book a call at https://cal.com/stuartb/30min.