While large language models (LLMs) have become incredibly capable, they remain effectively “trapped” behind chat interfaces or siloed API calls. When you try to give an agent the power to actually do work, e.g. querying a database, checking a CMS or browsing a local file system, you usually end up building brittle, point-to-point integrations.
This is the barrier that Model Context Protocol (MCP) is designed to remove.
I suspect that the next couple of years will be about the “universal port” that connects models to the real world. As we shift toward a fully composable architecture, an approach where business systems are built from independent, interchangeable modules that communicate via APIs, MCP is emerging as the intelligent orchestration layer that will define how enterprise AI scales.
What Is Model Context Protocol (MCP)?
MCP is an open-standard interface that acts as a “universal translator” between AI models and external data sources. It allows developers to connect agents to filesystems, databases and APIs using a single, repeatable protocol, eliminating the need to build custom, brittle integrations for every new tool or data silo.
What MCP Actually Is — and Why You Should Care
MCP is an open standard that decouples the AI “client” (the LLM or agent framework) from the “server” (the data source or tool). By providing a standardized interface, MCP allows any agent to navigate file systems, query databases or trigger APIs without the developer writing custom integration logic for every single tool. It provides the abstraction layer that has been missing from the stack.
MCP enables a bidirectional exchange between the model and the data source. This is a significant departure from traditional REST APIs. In a REST setup, the client (the AI) has to know exactly what it's looking for. With MCP, the server can expose a schema of its capabilities, and the model can discover how to use those tools on the fly. This self-describing nature is what makes it a true protocol rather than just another API wrapper.
The Architect’s View: Moving to Composable AI
We’ve been talking about “composable enterprise” architecture for years. This term refers to the idea that business functions should be modular components. But AI has historically been a black box that sits outside this modularity. This is largely because most AI integrations have been built as one-offs, deeply hard-coded into specific applications with proprietary wrappers that don’t play well with others. Without a common language to talk to your existing data, AI becomes a siloed bolt-on feature rather than a seamless part of your infrastructure.
MCP changes the game by acting as the intelligent orchestration layer. It sits between your enterprise components, such as your event streams, your CMS, your legacy databases and your AI agents.
It provides a central point to manage what an agent can and cannot see. It allows you to swap an underlying LLM (say, moving from OpenAI to Anthropic) without rewriting your entire data access layer. And, finally, it enables the common context needed for an agent to understand not just the data, but the rules surrounding that data.
Real-World Patterns: What We’re Seeing Now
In my work, I’m seeing two dominant patterns emerge among early adopters who are moving away from “brittle” AI.
Instead of building a GitHub agent or a Salesforce agent, teams are building MCP servers that act as adapters. These servers wrap existing internal knowledge bases or private APIs and expose them via the MCP interface. Now, any agent the company deploys can immediately plug in to that data.
We’re seeing MCP clients embedded directly into developer tools and IDEs. Tools like Cursor and Zed have already begun integrating MCP, allowing an assistant to pull live code context, documentation and Jira tickets through one protocol. This eliminates context switching, which studies suggest can cost developers up to 40 percent of their productive time.
The Content Orchestration Use Case
Consider a content editor in a complex enterprise environment. If the editor wanted an AI to validate a new product launch, that AI would need manual, hard-coded connections to a CMS (for the text), a DAM (for the images) and a style guide repository (for compliance).
The AI would need to verify that the marketing copy in the CMS matches the technical specifications in a Product Information Management (PIM) system, while ensuring the images in the DAM system have the correct usage rights for the target region. Traditionally, engineering and product teams would have to build and maintain three separate, hard-coded “bridges” for the AI to talk to these systems. Every time the CMS updated its API version or the DAM changed its metadata schema, these bridges would break, requiring manual developer intervention.
With MCP, the assistant uses a single protocol to query these disparate systems. It can pull metadata from the DAM and taxonomy rules from the CMS in a single pass.
The result: Dramatically fewer round trips, lower latency and, most importantly, coherent consistency across systems that don’t usually talk to each other. Because MCP allows these disparate systems to expose their context in a unified format, the AI can detect a hallucination or a factual error before the content goes live. It turns isolated silos, which normally never talk to one another, into a single, unified knowledge graph that the AI can navigate with perfect logic.
The Roadblocks to Deployment
The biggest hurdle right now is trust. Organizations are rightfully terrified of giving an autonomous agent a metaphorical skeleton key to internal systems. And many dev teams are still stuck in the mindset of building custom integrations for every use case. Shifting to an open, shared protocol requires a level of architectural maturity that many are still developing. Some people may view a unified protocol may as a security risk rather than a governance tool. For organizations that want to overcome this hurdle, I recommend the following three-pillar trust framework.
First, you should start with identity-first security and use AI agents as first-class citizens in the identity stack. Instead of shared service accounts, it’s more effective to move toward unique agent identities with short-lived, scoped tokens. MCP facilitates this by supporting OAuth 2.1, allowing security teams to apply the same granular, least-privilege policies to an agent that they would apply to a human employee.
Second, you can complement this with governed autonomy, where trust is built by defining decision boundaries. This means implementing human-in-the-loop checkpoints for high-risk operations while allowing the agent full autonomy for read-only or low-risk analysis. MCP’s self-describing nature allows these guardrails to be enforced at the protocol level, blocking dangerous tools before the agent even sees them.
And finally, a phased adoption roadmap is more likely to provide stability. You’re more likely to be successful if you start with read-only discover for the agents, then move to assisted Action where a human must approve an AI action, and once that’s been tested multiple times, only then can you reach policy-aware autonomy, i.e., self-governing, intelligent agents.
Looking Ahead at the Composable AI Era
In the next two to three years, I expect MCP to become the de facto connective tissue for the enterprise. We are moving toward a federated agent ecosystem.
In this world, you won’t have one giant AI. Instead, you’ll have a fleet of specialized agents that discover shared services via MCP, crossing system boundaries (analytics, content, personalization) while maintaining a strict audit trail.
For those of us building in this space, the message is clear: The era of the siloed chatbot is over. The era of the composable, protocol-driven agent is here. Early adopters aren’t just building faster; they’re building systems that are actually scalable, resilient and ready for what the next generation of LLMs brings.
