Note: This is the third of a series of five posts I’ll be making this week on MCP and how I see its transformational impact on the architecture for the next generation of AI systems.  Check out the first post here on MCP as the Open API Standard for the AI Era.  Post 2 here on The New Vocabulary of AI Orchestration

Post 3 in the Series: Introducing MCP to Marketing Tech Leaders

By Dan Jaye, CEO

Building on the sampling and elicitation concepts I introduced in my previous post, let’s examine how these interaction patterns scale into something much bigger: chainable AI architectures that can transform entire technology stacks.

I had a realization last month: I was spending more time thinking about orchestration than implementation. That’s when it struck me – we’re experiencing the same architectural transformation that revolutionized web development, but compressed into a timeframe that’s frankly breathtaking.

With MCP adoption accelerating – we’ve gone from Anthropic’s announcement in November 2024 to over 5,000 active servers by May 2025 – we’re not just enabling integrations anymore. We’re enabling real-time composition of AI capabilities.

Imagine Your Marketing Stack as Living Architecture

So, let me paint you a picture of what this means practically. Say you’re architecting a customer engagement system. In the old world, you’d hand-code integrations with your CRM, recommendation engine, analytics dashboard, and content optimization platform. Each integration would be a custom implementation, brittle, expensive to maintain, and difficult to modify.

In the MCP world, each of these becomes a standardized server. As a result, your MCP client can dynamically discover, evaluate, and route workflows across these services. Want to A/B test different sentiment analysis approaches? Your MCP client discovers a new sentiment server in a registry, evaluates its capabilities, and adjusts behavior – all without shipping new code.

This is chainability in action, and it’s unlocking entirely new approaches to software architecture.

The Trust Infrastructure Challenge

This compositional flexibility introduces a sophisticated challenge: trust at scale. When your AI agent discovers three candidate servers for predicting customer churn, which should it choose? Who verifies that an MCP server performs as advertised? Who governs privacy compliance across dynamic integrations?

In highly regulated industries, every AI-generated answer and the data informing it must be auditable. With MCP, every LLM response can be traced back to its data sources through a single governance layer.

Therefore, we need something analogous to certificate authorities – just like the early programmatic advertising ecosystem required trust signals for inventory quality. The MCP ecosystem demands governance frameworks: certifications, security audits, performance ratings, compliance verification.

What This Means for Marketing Tech Leaders

For marketing technology leaders, this means tools like CDPs, attribution engines, and dynamic content optimizers become interchangeable components in an MCP architecture. They can be dynamically swapped, chained together for complex workflows, or composed in real-time based on campaign requirements.

Major enterprises are already recognizing this potential. Companies like Goldman Sachs and AT&T have implemented AI models compatible with protocols like MCP to streamline business functions including customer service and code generation.

The organizations building modular, discoverable, composable AI tools will dominate the MCP future. This isn’t a technical curiosity – it’s a go-to-market strategy that will determine market position in the next wave of marketing technology.

As I’ll demonstrate in my next post about simulation-driven development, this architectural flexibility also creates new opportunities for testing and validating AI systems before deployment.

Move to Post 4 in the Series Here: From Prototype to Production: Simulating AI Solutions with AI