The conversation around AI is shifting. We’ve moved beyond novelty demos and “look what it can do” moments. In the enterprise, what matters now is utility, reliability, and interoperability. It’s not enough for an AI agent to be impressive — it has to plug into the real systems where work happens.
That’s where the Model Context Protocol (MCP) comes in.
Originally introduced by Anthropic, MCP was designed to standardize how AI models discover and interact with external tools. The protocol quietly gained traction through developer-first tools like Cursor and Windsurf (formerly Codeium). But then OpenAI — Anthropic’s direct rival — publicly adopted MCP in March 2025. That move sent a clear message: this isn’t just another experimental framework. MCP is on its way to becoming the default standard for how AI models with integrate with external data and tools.
And that changes everything.
To understand the value of MCP, you have to start with the problem: traditional APIs aren’t built for AI.
They’re deterministic. Rigid. Unforgiving. AI models, especially large language models (LLMs), are probabilistic — they guess. That mismatch leads to hallucinated parameters, broken integrations, and failed tool calls.
MCP fixes this by acting as a translator. It defines a common language that models can use to reliably call tools — whether it’s a database query, a system command, or a SaaS integration. It lets agents know what tools are available, how to use them, and how to maintain context across those interactions.
The result? Agents that don’t just talk — they act. Consistently.
In simple terms, MCP connects an agent to tools it needs to get work done. That could mean querying a database, calling an internal API, pulling a document from a shared drive, or kicking off a workflow in a third-party system. MCP is the bridge that makes those interactions possible – and consistent – across tools, models and environments.
Here’s how it plays out:
The host is the application that embeds the agent. Think: a chat app, an internal portal, or a smart assistant built into a business tool.
The client acts like a middleman, handling communication between the agent and whatever tool it needs.
The server is where the actual tool lives – it could be connected to your CRM, financial system or internal knowledge base.
Let’s say someone asks the agent, “How many customers do we have in New York?” Behind the scenes, MCP helps the agent understand which tool to use (maybe a customer database), formats the request correctly, and executes it. It then takes the response, feeds it back into the model and returns a usable, context-rich answer to the user.
The exciting part is that it abstracts away the complexity. It doesn’t matter if your data lives in SQL, a REST API, or a file share – the agent sees one standard way to access it. And your teams don’t have to build new integrations for every tool or every model.
Since agents are only as powerful as the tools they can reliably access, MCP gives enterprises a path to make internal systems “agent ready” without locking into any one LLM, vendor or platform. This start to open the flood gates of use cases in the enterprise, here’s where we’re seeing traction:
For enterprise leaders, MCP represents more than just technical progress — it’s a strategic unlock.
Standardization: Build once, use across models and departments
Reusability: Internal tools can be exposed as MCP servers and used everywhere
Security: Centralized oversight and control over what tools agents can access and how
Future-proofing: As models evolve, your integrations don’t have to — they speak a shared language
The shift is subtle but powerful: the enterprise isn’t building for a single model anymore. It’s building a protocol-level foundation for AI integration that’s reusable, extensible, and governable.
But with New Power Comes New Risk
MCP lowers the barrier for agents to act — which means the guardrails matter more than ever. Enterprises adopting MCP must be equally focused on governance, trust, and semantic understanding. For example:
What does “net revenue” actually mean in your org?
Which agents are authorized to access which tools?
How do you prevent misuse — accidental or otherwise — when tool invocation is one prompt away?
Who is managing requests for API keys?
Are there malicious MCP servers connected to the environment?
This is where Shadow AI risks get real. When anyone can spin up an MCP server and wire it to production systems, visibility and oversight are essential.
We’re excited about where this is going — genuinely. MCP has all the makings of a foundational standard for the agent-driven enterprise. But it’s worth saying out loud: today, getting started with MCP can feel... delicate.
Setting up servers often means dealing with fragile JSON files, finicky tool configurations, and precision in places where LLMs like to guess. One wrong parameter, a missing schema, or an overlooked file path — and things just don’t work.
And if you’re inside a large organization, it’s never just one server. Suddenly, every team wants to expose a new API, make a data set agent-accessible, or spin up a tool that talks to Salesforce, Workday, or Snowflake. The requests start coming from every angle — and without strong governance, your clean protocol can turn into a sprawling mess.
This is the in-between moment we’re in:
The standard is here.
The benefits are clear.
The experience? Still catching up.
But let that not deter the path. These are the normal growing pains of any infrastructure shift. What matters is that MCP is doing the hard work of creating common ground — a way to make agents not just intelligent, but interoperable.
We’ll look back on these clunky early days the same way we look at early container configs or cloud templates. It wasn’t about the YAML. It was about building something scalable — and eventually seamless.
MCP isn’t perfect yet, but it’s progress. And for the enterprise, it’s the next step toward an evolved, agent-ready architecture.