I’ll be honest. Eight months ago, I had no idea what Model Context Protocol was. Then I was in a meeting where someone casually mentioned “MCP,” and everyone nodded as if it were obvious. I smiled and nodded too. (You know that moment.)
Later, I went down the rabbit hole. Here’s what stood out after 20 hours of research: MCP isn’t just another AI acronym to memorize. It is what makes or breaks whether your AI projects stay stuck in pilot purgatory or actually ship to production.
I’m writing this to organize my own thoughts, save you the 20+ articles I had to read, and because we’re all figuring this out together.
The Problem I Keep Seeing (And Maybe You Do Too)
Last quarter, I watched a brilliant AI proof-of-concept die in committee. The AI wasn’t the problem. It was bright enough. But connecting it to our actual systems (CRM, property management software, the Excel spreadsheets everyone secretly still uses) would take six months and three developers.
I keep seeing the same pattern: Every AI tool needs custom code for every data source. The math is brutal: 10 AI tools × 10 data sources = 100 custom integrations to build and maintain.
I started calling it the “integration tax.” You pay it every single time. And it’s why so many AI projects never make it past the pilot stage. Not because the AI isn’t capable, but because the logistics of connecting it to real business data are exhausting.
Here’s what makes everything particularly frustrating: We’re at an inflection point where AI deployment is finally possible at scale. But integration velocity is the bottleneck. Leaders who solve how AI tools connect to enterprise data will pull ahead in ways brutal to catch up to.
And that’s where MCP comes in.
So What Actually Is MCP?
Everyone uses the USB-C analogy, and I’m going to use it too. Remember when every device needed a different cable? MCP is trying to do for AI integrations what USB-C did for charging: create a standard protocol that works.
Anthropic, the company behind Claude, open-sourced MCP in November 2024. It’s not a product you buy. It’s a protocol, like HTTP or USB. A universal bridge between AI models and business systems.
The Three Core Components of AI System Architecture
1. The Host is your AI application (Claude, ChatGPT, whatever you’re using). Where the AI actually lives and thinks.
2. The MCP Client is the translator. It speaks both languages: AI language on one side and data source language on the other. Think of it like having an excellent executive assistant who knows how to talk to everyone in the company. The client handles all the AI connectors and communication logistics.
3. The MCP Server connects to your actual stuff: databases, APIs, file systems, that SharePoint drive everyone hates but still uses. You might have one server connected to your CRM, another to your property database, and another to your email system. Each server acts as an AI connector to a specific data source.
Why It Matters for Enterprise AI Systems
Here’s what clicked for me: You write one MCP server for your CRM, and suddenly ANY AI tool that supports MCP can talk to it. You don’t rebuild the integration for Anthropic Claude, then again for OpenAI GPT, then again for whatever comes next.
Vendor flexibility, faster deployment, lower cost. If Claude raises their prices, you’re not locked in because you’ve built 50 custom AI integrations. Your MCP servers work with whatever AI model you choose. No more vendor lock-in.
David Soria Parra and Justin Spahr-Summers, the two engineers at Anthropic who created MCP, built it because they were constantly copying and pasting between Claude Desktop and their IDE. Six weeks later, they had a working prototype. That origin story (solving your own problem) leads to useful technology.
Where I See This Going: AI Adoption Strategy for Different Teams
For CRE Professionals
I’m working with a team that wants AI to pull property comps from three different databases. Right now, that’s three custom integrations, probably three months of work. With MCP? It could be three afternoons.
The use case: you’re analyzing a potential acquisition, you ask your AI, “Show me comparable properties in the area,” and it pulls from your internal database and two third-party sources, then synthesizes everything. No switching systems. No copying and pasting.
I haven’t shipped this yet, so I can’t promise it’s that smooth. But that’s the vision.
For AI Operators: Governance and Data Access Control
The part that excites me: consistent security posture across all your enterprise AI systems. Instead of every AI tool connecting to your data differently, you’re standardizing how AI accesses and uses context through MCP servers.
You control what data the AI can see. You control what actions it can take. You log everything in one place. That’s powerful for AI compliance and AI governance.
Though I’m still working through what this means for our existing security protocols, those conversations are underway with our security team.
For Growth Teams: Accelerating AI Deployment
If you can deploy AI capabilities in weeks instead of quarters through interoperability, that’s a competitive edge. Not in a “cool tech demo” way, but in a “we’re serving customers faster” way.
Network effects interest me too: As more people build MCP servers, your AI gets more capable without you writing additional code. Someone creates an MCP server for Salesforce; you can use it. The ecosystem creates value for everyone through AI standardization.
Early Signals from the Enterprise AI Ecosystem
Anthropic launched MCP in November 2024. Four months later, OpenAI announced support. Then Google Vertex AI. Then Microsoft Copilot. That’s swift adoption for an AI protocol.
Alex Albert, who leads developer relations at Anthropic, noted that “in less than 4 months, MCP has gone from just an idea we had at Anthropic on how to make integrations easier for devs to the industry standard for all AI app integrations.”
Whether the momentum continues is anyone’s guess. But right now, something real is happening in AI infrastructure.
Three Things I Got Wrong About AI Implementation
Misconception #1: “It’s Just Another API Standard”
My first reaction: “Great, another protocol to learn. We already have REST APIs, GraphQL, gRPC…”
But MCP is more like HTTP—a foundational standard that other things build on. It’s infrastructure, not implementation. The difference matters because AI infrastructure sticks around while specific implementations come and go.
Misconception #2: “We Need to Rip and Replace Everything”
Adopting MCP meant rebuilding our entire AI stack. That sounded exhausting.
Reality: You can run MCP alongside existing AI integrations. Test it with one painful integration. Prove the value. Then expand. Start small with your AI project rollout.
Misconception #3: “MCP Replaces RAG”
I spent a week thinking that MCP and RAG (Retrieval-Augmented Generation) were competing approaches in an AI protocol comparison.
Nope. MCP and RAG are complementary:
- RAG is about how you retrieve relevant information from a large corpus
- MCP is about how you connect to data sources in the first place
Use MCP to connect to a document store, then use RAG to find the proper documents, then use AI to synthesize an answer. They work together.
If You Want to Explore This: Practical AI Adoption Guide
I’m not saying implement MCP tomorrow. It’s worth understanding what’s happening, even if you’re not ready to act yet.
Find Your People
Best move I made: reached out to three people who mentioned MCP in their posts and asked to pick their brains for 20 minutes. Two said yes. I learned more from those conversations than from ten articles.
The AI community right now is surprisingly generous with knowledge-sharing. Please take advantage of it.
Worth following: Alex Albert at Anthropic, Simon Willison (writes detailed posts about AI tools and security), and the official MCP GitHub organization.
Test Something Small
My plan: one small pilot with our team. One integration, one use case, measured carefully. Not a company-wide rollout. An experiment to see if the benefits are real in our context.
My success metric: How much time does this save versus our current process? If it’s even 30-40% faster, that’s worth scaling.
Stay Curious About AI Infrastructure
I don’t need to understand every technical detail. But I need to understand the strategic implications well enough to make informed decisions about where to invest time and resources.
And I need to stay curious as the space evolves. Because it is growing fast, what’s true today might be outdated in three months. Very much like SEO, sound familiar?
The Bigger Picture: Emerging AI Infrastructure
Developers are building new infrastructure in real time. Developers built most of the internet’s infrastructure layer (HTTP, REST APIs, JSON, and OAuth) years ago. But AI is creating the need for new infrastructure.
MCP might be part of how we answer questions like: How do AI systems connect to data? How do they use tools? How do they maintain context across systems?
The pattern I keep coming back to: APIs in 2005, cloud computing in 2010. The people who understood these shifts early didn’t become technical experts, but they understood the strategic implications. They could see around corners.
The integration problem is real. AI hitting the “deployment gap” is real. Something will solve this, whether it’s MCP or something else. Being part of the conversation now means you’ll understand the solution when it arrives.
Frequently Asked Questions About MCP
What is Model Context Protocol (MCP)? MCP is an open-source, standardized protocol for AI integration that provides a universal bridge between AI models and business systems. Think of it as USB-C for AI. One standard that lets any AI tool connect to any data source without custom code for each combination.
How does MCP differ from traditional API integrations? Traditional APIs require custom code for every AI tool and data source combination (creating the “integration tax”). MCP provides a standardized way for AI to access data, so you build one MCP server, and any MCP-compatible AI can use it. It eliminates vendor lock-in and accelerates AI deployment.
Do I need to rebuild my existing AI integrations to use MCP? No. You can run MCP alongside existing integrations. Start with one painful integration as a pilot, measure the results, and expand gradually. It’s an incremental approach, not a rip-and-replace scenario.
Is MCP secure for enterprise data? MCP provides a framework for standardizing how AI accesses and uses context, thereby improving the security posture by centralizing access control, permissions, and audit trails. However, each organization must implement appropriate authentication, encryption, and governance policies for its specific MCP servers.
Which AI platforms support MCP? As of early 2025, Anthropic Claude, OpenAI GPT, Google Vertex AI, and Microsoft Copilot have all announced support for MCP. The protocol is open-source, so more platforms are adopting it rapidly.
What’s the relationship between MCP and RAG (Retrieval-Augmented Generation)? They’re complementary, not competing. MCP handles how AI tools connect to enterprise data sources (the connection layer). RAG handles how AI retrieves relevant information from those sources (the retrieval method). You often use both together.
How long does it take to implement an MCP server? The original creators built a working prototype in six weeks. For a simple use case (like connecting to a single database), teams are reporting implementation in days rather than months. Complexity depends on your data source and security requirements.
What are the main benefits of MCP for enterprise AI systems? Key benefits include: faster AI deployment through interoperability, elimination of vendor lock-in, consistent AI governance and data access control, reduced development costs, and the ability to leverage a growing ecosystem of pre-built MCP servers.
Final Thoughts
I’m not an MCP expert. I’m a curious professional trying to understand an emerging tool that might matter a lot. I’ve spent a few dozen hours reading, testing, and talking to people who know more than I do. And I’m definitely still learning.
What I hope you take away:
- The “integration tax” is a real bottleneck, and solving it creates a competitive advantage
- MCP is one potential solution worth understanding and watching
- Stay curious and keep learning, because this space is moving fast
If you’re working with MCP in production, I’d love to hear what you’re learning. If you’re starting to explore, I’d be happy to compare notes. If you think I’m off base on something, even better. Tell me why. That’s how I learn.
Here’s what I love about this moment: We’re all figuring it out together. No “experts” with 10 years of MCP experience exist, because it’s been around for only 6 months. Your questions are just as valid as anyone else’s.
So ask the questions. Build the small tests. Share what you learn. And don’t forget to enjoy the process. The fun part of being in tech is getting in early and deciding what matters.
Questions? Experiences? Things I got wrong? Reach out – I’m always up for a conversation. We’re all learning together.
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