The diversity of MCP implementations – from enterprise giants like Spring AI MCP to creative innovators like Figma MCP – reflects the protocol’s versatility. As AI adoption skyrockets, these MCPs are reducing integration friction, cutting costs, and enabling AI to deliver hyper-relevant results across industries.
Imagine browsing a marketplace where you can discover, evaluate, and deploy marketing AI components like app store applications. A/B test different attribution models. Swap personalization engines without rewriting integration code. Deploy new optimization algorithms and measure performance impact immediately.
This isn’t speculative – it’s the logical evolution of MCP adoption in marketing technology.
How do you test complex, interconnected AI systems before they go live? I’ve been experimenting with something that initially sounded absurd but has become indispensable: using AI to test AI systems.
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.
The organizations that master these new approaches via a new vocabulary – sampling, elicitation and completions – will architect the systems that define the next generation of AI-powered marketing technology.
I’ve been thinking a lot about Model Context Protocol (MCP) for months now, and I keep returning to one thought: MCP is to generative AI what Open API and Swagger were to the modern web.
The next wave of value creation won’t come from pushing more data into more dashboards. It’ll come from systems that make your data understandable to AI, useful to humans, and aligned to your business in real time.
The partnership addresses one of the most significant challenges in today’s privacy-first advertising ecosystem: seamless data interoperability across diverse marketing and advertising technology platforms.
AI agents operate based on context windows – temporary, transient memory banks that let them hold a conversation or execute a task. These windows are not durable. Once the window closes or the session ends, that “personality” disappears.