What to Know About Model Context Protocol (MCP)

Model Context Protocol (MCP) has quickly emerged as a topic of interest in conversations about AI for business. This post explains the significance of MCP, how the technology works, its revolutionary aspects, and how organizations can position themselves to use it.

MCP was released by Anthropic last November, described as “a new standard for connecting AI assistants to the systems where data lives, including content repositories, business tools, and development environments.” [1]

MCP is significant because it directly addresses a problem that AI agents present for most organizations: interconnectivity. In an ideal framework, AI agents can be sequenced to work together while utilizing shared tools and information. This is often described as orchestration, and in that sense, an MCP is like the sheet music that gets passed around to all of the players in the orchestra before a concert.

Sheet music comes in a standardized language that the musicians in an orchestra can understand. Each player’s set of instructions is different, telling them which instrument to use, when to use it, and how. In this way, MCP can make it far easier for AI agents to communicate with the other elements in an organization’s technology ecosystem.

What is the Model Context Protocol for 2026?

The Model Context Protocol has evolved into much more than just a technical specification, it’s become the backbone of how AI systems will connect to real-world business processes. MCP represents the industry’s answer to a fundamental problem: how do you make AI agents work seamlessly with your actual business tools? It operates like the universal translator for your entire tech stack. It doesn’t matter whether you’re using Google Drive, Salesforce, or a custom legacy system — MCP creates a standardized language that AI agents understand. 

Open AI standards like this are transforming enterprise integration. Instead of building custom connections for each tool, organizations adopting the MCP framework can plug in pre-built servers immediately. The real power? Model interoperability across vendors becomes possible, eliminating proprietary lock-in.

Benefits of MCP for AI Orchestration in the Enterprise

Here’s what excites enterprise leaders about MCP: it makes AI orchestration work at scale. Without AI context sharing through standardized protocols, you’d spend months building custom integrations for each tool. With MCP, you connect once and reuse everywhere. The benefits are faster deployment, fewer bugs, lower maintenance costs. 

Your AI agents can share context seamlessly across your entire technology ecosystem without developers rewriting integration code repeatedly. Organizations implementing the Model Context Protocol report 40-60% faster agent deployment times. 

Security improves too, with clear rules about what data agents can access; there’s no vendor lock-in. You’re working with an open standard that works with any model or framework. As a result, your team moves faster, systems integrate better, and your AI agents become genuine multipliers of human capability. 

MCP Architecture in a Nutshell

MCP’s architecture is simple, which is why it’s so powerful. At the core, you have AI agents on one side and data sources or tools on the other. Between them sits the MCP framework, which acts as a translator using JSON (JavaScript Object Notation) for communication. When an agent needs something, including data, an action, or context, it sends a standardized request to an MCP server. That server knows exactly what the agent is asking for because they’re speaking the same language. The server retrieves the information or executes the action, then sends back a standardized response. 

Figure 1: MCP Deep-Dive

Source: Anthropic

MCP servers exist in an open-source repository and Anthropic has shared pre-built servers for enterprise systems like Google Drive, Slack, and GitHub, Git, Postgres, and Puppeteer. Because MCP is open-source, it’s technology agnostic and anyone can experiment with it using their own tools and models.

Advantages of MCP include:

• More flexible and scalable than custom API integrations,
• Compatible with frameworks like LangChain and Agents,
• Compatible with an open technology ecosystem that integrates market-best tools and models.

“Without MCP (or something like it), every time an agent needs to do something in the world — whether fetching a file, querying a database, or invoking an API — developers would have to wire up a custom integration or use ad-hoc solutions,” Ksenia Se wrote in her post for Hugging Face. “That’s like building a robot but having to custom-craft each finger to grasp different objects — tedious and not scalable.” [2]

MCP vs. RAG: Key Differences

People often confuse MCP with RAG (Retrieval-Augmented Generation), but they solve different problems. MCP is a two-way protocol that lets AI agents actively interact with external systems, trigger actions, and modify data. RAG is a one-way approach that retrieves relevant information and feeds it into the model before generating a response. 

Here’s what sets them apart:

• Interaction Model: MCP is stateful (maintains context across multiple interactions); RAG is stateless (each query stands alone).
• Scope: MCP orchestrates workflows and coordinates across multiple systems; RAG augments what’s inside the model’s brain.
• Real-World Action: MCP enables agents to actively change systems and data; RAG focuses on knowledge enhancement.
• Use Cases: MCP for enterprise orchestration and integration; RAG for knowledge retrieval and context enhancement.
• Best Practice: Sophisticated AI systems use both: MCP for orchestration, RAG for knowledge enrichment.

The key difference is that RAG fills the model’s context window, while MCP connects the model to the real world.

What are the Latest Advancements in MCP Technology?

MCP is becoming as fundamental to AI infrastructure as APIs are to traditional software development. Here’s what’s happening right now:

• Major Platform Support: OpenAI announced MCP support across all products including ChatGPT desktop app; Google launched Agent2Agent (A2A) protocol as MCP complement.
• Ecosystem Growth: Over 1,000 community-built MCP servers now exist, covering Google Drive, Slack, databases, and custom enterprise systems.
• Advanced Orchestration: Multi-agent workflows through MCP enable agents to coordinate complex processes without centralized control.
• Enhanced Security: Better authentication, encryption, and permission management built into model context protocol; enterprise-grade security features.
• Performance Improvements: Optimization for larger context windows, faster response times, and handling more complex agent interactions.
• Enterprise Adoption: 50+ partners including Salesforce, ServiceNow, Workday, and consulting firms like Accenture and Deloitte leading implementation.

Does MCP Change Everything?

Providing a standardized interface for AI agents to communicate with a broader ecosystem of information and software is revolutionary. Still, the initial release of MCP went largely unnoticed until earlier this year, when MCP seemed to eclipse AI agents as the focal point of marketplace attention.

“MCP is bigger as an idea than it is as an actual technological achievement,” says Robb Wilson, CEO and co-founder of OneReach.ai, noting that the real revolution comes with the trajectory MCP opens. “Its implications and where it’s going is what’s exciting.”

Those implications relate to how the MCP closes the gap between LLM-based AI agents and real-world business systems and information. Block (Square), Apollo, Zed, Replit, Codeium, and Sourcegraph, were early MCP adopters, and the ecosystem now has more than 1,000 community-built MCP servers.

Add to this growth in the MCP ecosystem Sam Altman’s announcement last month that OpenAI will support MCP across its products, including the desktop app for ChatGPT. [3] Just days ago, Google released their own Agent2Agent (A2A) protocol that they describe as a complement to MCP. The tech giant cited support from 50+ partners, including Atlassian, Intuit, PayPal, Salesforce, ServiceNow, Workday; and leading service providers like Accenture, BCG, Capgemini, Cognizant, Deloitte, McKinsey, and PwC. [4]

This surge of interest and activity is noteworthy, but it points to something bigger than a single protocol. With traditional software, various tools and features are bundled by graphical user interfaces (GUIs). Agentic AI puts us on the cusp of a world where anyone can turn to a piece of technology and simply ask for help. Behind the scenes, a flurry of activity that includes MCP (or something like it) brings back the information or action requested. In this scenario, the GUI software bundle loses all relevance.

As Wilson suggests, MCP breaks software into pieces: tools for hire that users will only care about in the moment that they are needed. “What we’re talking about is a single UI for all our software. That’s massive. If we’re talking about one UI that you can use to get a bunch of stuff done, people are going to want to own that UI. OpenAI thinks and hopes they will, Anthropic hopes and thinks they will.”

How to Leverage MCP

Wondering who might end up owning a lone UI perched high on a distant mountaintop clearly isn’t top-of-mind for businesses at this moment, but it does point to a critical factor that organizations have to consider as they are assembling a framework for agentic AI. The alternative to one UI for all software, is individual organizations with UIs that are connected to their unique software ecosystem.

The backend of a truly dynamic and useful organizational UI needs to be both open to new technologies and flexible enough to rearrange itself around any new requirements that come with them. MCP is open and model-agnostic, which aligns with these requirements, but its sudden rise to prominence is also a reminder that in this new era of conversational technologies, the idea of “market-best” is completely fluid. Revolutionary tools and approaches will continue to erupt and stumble over one another as agent orchestration matures.

A world filled with high-functioning tech ecosystems might seem like a distant promise, but the race toward them is already underway. MCP is a key piece in this journey, both in the way that it standardizes communication between machines and in the way that it can contribute to an open ecosystem, where any tool or data source can become part of a bigger process automation.

The future of technology is open, and OneReach.ai’s GSX platform simplifies AI agent deployment by ensuring seamless integration with existing IT tools and systems, enabling real-time AI Agents orchestration for optimal performance. GSX empowers organizations to model, implement, operate, monitor, and optimize their long-running processes.