What is MCP? A Business Guide to Model Context Protocol

AI models can generate answers, but they cannot take meaningful action without access to external systems. That limitation has slowed the adoption of AI agents in real business environments. Every new connection requires custom integration, which increases cost and complexity.

Model Context Protocol (MCP) solves this problem by providing a standardized way to connect AI systems to tools, data sources and applications. Instead of building one-off integrations, organizations can use MCP to enable consistent, scalable connectivity across AI applications.

What is MCP?

Model Context Protocol (MCP) is an open standard that enables AI models to connect to external systems, tools and data sources through a standardized interface. It allows AI systems to move beyond static training data and interact with live systems in real time.

Before MCP, developers had to build custom integrations for every AI tool and every external service. That created what many describe as an exponential integration problem. MCP addresses that challenge by defining a consistent way for AI applications to discover tools and retrieve relevant information to execute appropriate actions.

A simple way to understand MCP is to think of it as a universal connector for AI. Just as a USB-C port allows different devices to connect via a standard interface, MCP provides a standardized way for AI systems to connect to external resources.

How MCP Works in Practice

Learn how Jitterbit MCP provides a secure, governed foundation to enable scalable, reliable, and secure agentic AI.

MCP uses a client-server architecture that enables communication between AI systems and external tools. Think of it as a layered AI architecture with models, tools and data access. By standardizing data exchange, MCP removes the need for repetitive integration work and allows developers to focus on building useful AI applications.

There are three key components at work here:

1. MCP Host: The environment where the AI runs, such as Claude Desktop, an AI-powered IDE or another AI application. The host manages connections and orchestrates interactions.
2. MCP Client: The component inside the host that communicates with MCP servers. It sends API requests, manages sessions and formats requests using a standardized protocol such as JSON-RPC.
3. MCP Servers: Programs that expose tools, resources, and prompts to AI systems. MCP servers expose capabilities that allow AI agents to fetch data and interact with external services in order to perform tasks.

Will MCP servers replace APIs?

As AI agents become more common, MCP is quickly emerging as the foundation for connecting AI to real-world workflows. This raises an important question for many teams: will MCP replace APIs?

The short answer? No — MCP does not replace APIs. Instead, it often works alongside them by giving AI agents a standardized way to discover and use tools, many of which still rely on APIs behind the scenes.

What’s driving the shift to MCP?

While a lot of the hype around MCP has been fairly recent, the Model Context Protocol framework was actually introduced by Anthropic (the company behind Claude) back in November 2024.

It wasn’t until OpenAI (the company behind ChatGPT) officially adopted MCP in March 2025 that the protocol went from product feature to universal industry standard. Since then, MCP adoption has continued to grow, largely driven by the following:

Growing investment in agentic AI

Gartner estimates that by of enterprise software applications will include agentic AI a huge shift when you consider number was less than in But very same analysis from also predicts over projects be canceled the end either due to inadequate risk controls or inability prove>

MCP servers give AI agents access to the business data and tools they need to work effectively and truly autonomously, helping to close the ROI gap. At the same time, though, basic MCP servers without robust security guardrails can create new vulnerabilities, so it’s important that businesses take a proactive approach to authorization and monitoring.

Changing governance frameworks

As more workflows shift to autonomous agents, the governance model must evolve from rules that govern how users interact with applications to frameworks that govern how agents interact with applications.

Enterprise MCP servers provide that governance layer through standardized permissions, scoped access, audit trails, and human-in-the-loop controls that make agent-to-app interactions secure and observable.

Benefits of MCP

MCP offers practical benefits for organizations building AI applications and scaling AI systems, including:

Reduced integration complexity

MCP eliminates the need for custom integrations between every AI model and external system. Developers can connect AI applications via a consistent interface, eliminating the need to write boilerplate integration code.

Access to real-time data

CP enables AI systems to access external data sources and retrieve relevant information. This allows AI models to provide context-aware responses based on current data rather than static training data.

Faster development and deployment

By standardizing how AI systems connect to tools, MCP enables developers to build AI agents faster and deploy AI-powered applications more efficiently.

MCP Security Challenges

For enterprises, the challenge is not just understanding MCP; it is managing MCP access, governance and integration patterns in a way that fits existing business systems.

Security is a core requirement for using AI systems in MCP-fueled production environments. MCP introduces new risks by allowing AI systems to interact directly with external systems and sensitive data, including:

• Improper tool permissions that expose too much access
• Weak authentication across MCP server implementations
• Risks tied to connecting to external resources without governance
• Potential misuse of tool execution by AI agents

To address these risks, organizations need strong access control and identity management, secure communication across the transport layer, monitoring of AI activity and tool usage, and human oversight for high-risk actions.

Introducing Jitterbit MCP

Jitterbit MCP provides a secure foundation for AI automation at the enterprise level, replacing custom, ad-hoc bridges to external AI tools with a governed, standardized framework for agent connectivity.

Built on the Harmony platform, Jitterbit MCP seamlessly connects with the platform’s existing iPaaS, EDI, application development, and API management tools to expose existing business capabilities to AI agents, speeding up time to value for AI initiatives.

Connect with an AI expert to learn more about how Jitterbit MCP can help businesses operationalize AI with confidence.