In part one of this series, we introduced Model Context Protocol (MCP) as the “USB-C of AI,” a common standard that makes it easier for AI agents to connect with the tools and data you already use.
Now let’s take it one step further. Why should your business care? What do you actually gain? How does MCP compare to the alternatives already out there?
Before we get into the weeds of protocols and frameworks, let’s step back and think about what enterprises actually need from AI: flexibility, reusability, and future-readiness. MCP was designed with those goals in mind, and that’s why it offers advantages that go far beyond just “making integrations easier.”
Here’s how MCP translates into tangible benefits for businesses:
The value of MCP becomes even clearer when you look at how it could play out in different industries.
With MCP, enterprises can easily plug agents into the systems they already rely on, like Salesforce. Take a professional services firm that uses Salesforce for CRM: When a new agent is created, it can immediately access Salesforce to help sales reps prioritize leads and manage accounts. This reduces integration overhead, ensures consistent data governance, and makes it far easier to adopt new AI models as they emerge.
With MCP, retailers gain the flexibility to experiment with agents powered by different models, like GPT-4 or Claude, without duplicating integration work. A retailer can use a single MCP framework to connect both agents to its inventory system, order history, and loyalty database, enabling cross-model testing while keeping integration costs low. Traditionally, the integrations built for one would not work for the other. MCP gives the company flexibility to test multiple models without doubling its integration costs.
With MCP, healthcare providers can standardize integrations across multiple agents in a standardized and compliant way. A hospital network wants to implement agents that can access patient appointment data in a HIPAA-compliant way, including a scheduling assistant, an EHR-integrated doctor's aide, and a patient portal chatbot. Instead of rebuilding a new integration for each agent, the hospital leverages one secure MCP server that works across multiple agents, saving time and ensuring consistent governance.
With MCP, manufacturers can future-proof their agent integrations across diverse systems. On the factory floor, a manufacturing company wants to implement agents that can check machine logs, trigger maintenance requests, and analyze supply chain data. Today, these tasks live in separate systems. By exposing each system through MCP servers, the company ensures that as agents become more sophisticated, whether embedded in desktops, augmented reality devices, or Internet of Things (IoT) dashboards, they will be able to plug into the same set of capabilities without starting from scratch.
When companies start exploring AI integrations, MCP isn’t the only option they’ll encounter. Most large model providers already offer their own “function calling” APIs, and popular frameworks like LangChain or LangGraph are making it easier to orchestrate complex agents. So you might ask: Why do we need yet another standard?
The short answer is because none of the existing options are truly universal. Function calling is tied to specific vendors. Frameworks are powerful but aren’t standardized across the industry. MCP fills that gap by providing a model-agnostic, open protocol that works across different tools, agents, and environments.
Here’s how the landscape compares at a glance:
GenAI is moving fast, and as AI systems evolve from single-turn tools into persistent, multi-role agents, the need for standardized tool access grows. MCP addresses this need by providing a scalable, standardized approach to context management. By treating context as a structured, portable object, MCP facilitates smoother integration across agentic systems. It helps reduce vendor lock-in, promotes open standards, and makes orchestration easier, especially in complex environments involving pipelines, agents, and third-party tools.
MCP is quickly becoming the interface layer for agentic AI; it’s portable, governable, and increasingly supported across tools and platforms. Businesses that adopt MCP early will be well-positioned to experiment broadly, scale quickly, and stay in control as AI becomes more deeply embedded in daily work.