What is Model Context Protocol? The emerging standard bridging AI and data, explained

Chances are, unless you’re already deep into AI programming, you’ve never heard of Model Context Protocol (MCP). But, trust me, you will.

MCP is rapidly emerging as a foundational standard for the next generation of AI-powered applications. Developed as an open standard by Anthropic in late 2024, MCP is designed to solve a core problem in the AI ecosystem: How to seamlessly and securely connect large language models (LLMs) and AI agents to the vast, ever-changing landscape of real-world data, tools, and services.

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The AI company Anthropic explained that as AI assistants and the LLMs behind them have improved, “even the most sophisticated models are constrained by their isolation from data — trapped behind information silos and legacy systems. Every new data source requires its own custom implementation, making truly connected systems difficult to scale.” 

MCP was Anthropic’s answer. The company claimed it would provide a “universal, open standard for connecting AI systems with data sources, replacing fragmented integrations with a single protocol.”

That’s all well and good, but many companies have claimed that their universal standard would be the answer to all your technology problems. However, as the famous XKCD cartoon pointed out, if you have 14 different standards and then attempt to provide a single standard to fix everyone’s problems, you’ll soon have 15 different standards.

Also: Anthropic finds alarming ’emerging trends’ in Claude misuse report

It’s not that bad with AI integration protocols, programs, and application programming interfaces (APIs), but I could see it getting that way. At the moment, the other significant MCP rivals are Google’s Agent-to-Agent Protocol (A2A), workflow automation tools such as Zapier and Pica, and, of course, a variety of vendor-specific APIs and software development kits (SDKs). However, for reasons that will soon become clear, I believe MCP is the real deal and will quickly become the AI interoperability standard.

Let’s get to the meat of the matter.

What is MCP?

I view MCP as a universal AI data adapter. As the AI-centric company Aisera puts it, you can think of MCP as a “USB-C port for AI.” Just as USB-C standardized how we connect devices, MCP standardizes how AI models interact with external systems. To put it another way, Jim Zemlin, the Linux Foundation’s executive director, described MCP as “emerging as a foundational communications layer for AI systems, akin to what HTTP did for the web.”

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Specifically, MCP defines a standard protocol, built on JSON-RPC 2.0, that enables AI applications to invoke functions, fetch data, and utilize prompts from any compliant tool, database, or service through a single, secure interface.

It does this by following a client-server architecture with several key components. These are:

• Host: The AI-powered application (e.g., Claude Desktop, an Integrated Development Environment (IDE), a chatbot) that needs access to external data.
• Client: Manages a dedicated, stateful connection to a single MCP server, handling communication and capability negotiation.
• Server: Exposes specific capabilities — tools (functions), resources (data), and prompts — over the MCP protocol, connecting to local or remote data sources.
• Base protocol: The standardized messaging layer (JSON-RPC 2.0) ensures all components communicate reliably and securely.

This architecture transforms the “M×N integration problem” (where M AI apps must connect to N tools, requiring M×N custom connectors) into a much simpler “M+N problem.” Thus, each tool and app only needs to support MCP once for interoperability. That’s a real time-saver for developers.

How does MCP work?

First, when an AI app starts, it spins up MCP clients, each connecting to a different MCP server. These negotiate protocol versions and capabilities. Once it has a connection to the client, it then queries the server for available tools, resources, and prompts.

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With the connection made, the AI model can now access real-time data and functions from the server, updating its context dynamically. This means that MCP enables AI chatbots to access the latest data in real time instead of relying on pre-indexed datasets, embeddings, or cached information in an LLM.

So, when you ask the AI to perform a task (e.g., “What are the latest prices for a flight from NYC to LA?”), the AI routes the request through the MCP client to the relevant server. The server then executes the function, returns the result, and the AI incorporates this fresh data into your answer.

Additionally, MCP enables AI models to discover and utilize new tools at runtime. This means your AI agents can adapt to new tasks and environments without major code changes or machine learning (ML) retraining.

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In short, MCP replaces fragmented, custom-built integrations with a single, open protocol. This means developers only need to implement MCP once to connect AI models to any compliant data source or tool, dramatically reducing integration complexity and maintenance overhead. This makes a developer’s life much easier.

Making matters even more straightforward, you can use AI to generate MCP code and address implementation challenges.

Here’s what MCP provides:

• Unified, standardized integration: MCP serves as a universal protocol, enabling developers to connect their services, APIs, and data sources to any AI client (such as chatbots, IDEs, or custom agents) through a single, standardized interface.
• Two-way communication and rich interactions: MCP supports secure, real-time, two-way communication between AI models and external systems, enabling not just data retrieval but also tool invocation and action execution.
• Scalability and ecosystem reuse: Once you’ve implemented MCP for a service, it becomes accessible to any MCP-compliant AI client, fostering an ecosystem of reusable connectors and accelerating adoption.
• Consistency and interoperability: MCP enforces a consistent JSON request/response format. This makes it easier to debug, maintain, and scale integrations, regardless of the underlying service or AI model. This also means that integrations remain robust even if you switch models or add new tools.
• Enhanced security and access control: MCP is designed with security in mind, supporting encryption, granular access controls, and user approval for sensitive actions. You can also self-host MCP servers, allowing you to keep your data in-house.
• Reduced development time and maintenance: By avoiding fragmented, one-off integrations, developers save time on setup and ongoing maintenance, allowing them to focus on higher-level application logic and innovation. In addition, MCP’s clear separation between agent logic and backend capabilities enables more modular, maintainable codebases.

Who has adopted MCP?

The most important thing for any standard is: “Will people adopt it?” After only a few months, the answer is a loud and clear yes. OpenAI added support for it in March 2025. On April 9, Google DeepMind leader Demis Hassabis added his support. He was quickly seconded by Google CEO Sundar Pichai. Other companies have followed suit, including Microsoft, Replit, and Zapier.

This isn’t just lip service. A growing library of pre-built MCP connectors is emerging. For example, Docker recently announced it was supporting MCP with an MCP catalog. This catalog, not even six months after MCP was introduced, already includes more than 100 MCP servers from Grafana Labs, Kong, Neo4j, Pulumi, Heroku, Elasticsearch, and numerous others.

What are some real-world MCP use cases?

Beyond what Docker can access, there are already hundreds of MCP servers. These can be used for such tasks as:

• Customer support chatbots: AI assistants can access CRM data, product information, and support tickets in real-time, providing accurate, contextual help.
• Enterprise AI search: AI can search across document stores, databases, and cloud storage, and link responses to their corresponding source documents.
• Developer tools: Coding assistants can interact with CVS and other version control systems, issue trackers, and documentation.
• AI agents: And, of course, autonomous agents can plan multi-step tasks, act on behalf of users, and adapt to changing requirements by leveraging MCP-connected tools and data.

The better question, really, is what MCP can’t be used for.

The future: A universal AI integration layer

MCP represents a paradigm shift: from isolated, static AI to deeply integrated, context-aware, and action-capable systems. As the protocol matures, it will underpin a new generation of AI agents and assistants that can reason, act, and collaborate across the full spectrum of digital tools and data securely, efficiently, and at scale.

Also: How much energy does a single chatbot prompt use? This AI tool can show you

I haven’t seen any technology take off quite like this since generative AI itself first exploded on the scene in 2022. What I’m really reminded of, though, is how Kubernetes appeared just over a decade ago. At the time, many people thought there would be a race in container orchestrators between such now mostly forgotten programs as Swarm and Mesosphere. I knew from the start that Kubernetes would be the winner.

So, I’m calling it now. MCP will be the AI link that will unlock the full potential of AI in the enterprise, the cloud, and beyond.

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