What is MCP? The Model Context Protocol explained
Model Context Protocol (MCP) is an open-source standard that enables AI-powered systems to connect with software applications, tools, and platforms.
AI models are good at generating content and reasoning, but their capabilities are fundamentally limited by their training data. MCP addresses this limitation by allowing AI tools to access external resources when they need them.
MCP works through a two-way communication architecture. On one side are data providers and application developers who want LLMs to access data or perform actions within their software. They support MCP by building an MCP server, which exposes data and capabilities. On the other side are developers building AI tools or agentic systems, who implement an MCP client that allows their AI models to connect to MCP servers, retrieve context, and perform tasks across different applications.
For example, an AI coding tool like Cursor could include an MCP client that connects to MCP servers provided by Figma or GitHub. Through these connections, AI can move beyond generating static responses and instead perform dynamic workflows—such as retrieving documents, executing functions, running code, or interacting with external systems.
Communication between MCP clients and servers happens through a standardized protocol, similar to how HTTP defines communication on the internet. MCP specifies how messages are structured, what types of requests can be made, how authentication works, and how systems exchange context and actions.
The origin of MCP
MCP started at Anthropic in summer 2024 to give Claude Desktop an easier way to work with data sources like the local file system. The authors drew inspiration from Microsoft’s Language Server Protocol (LSP), the standard for how integrated development environments (IDEs) support features like context highlighting or code completion across many different programming languages.
After building the protocol and using it internally, Anthropic open-sourced MCP in November 2024, publicly releasing the full protocol specification along with documentation and SDKS (e.g., Python).
Since then, adoption has grown significantly. In January and February 2025, many AI IDEs, like Cursor and Windsurf, started supporting MCP. In March, OpenAI added MCP support, and GitHub released its MCP server soon after. Mature products are adopting MCP as well: Microsoft Windows announced it will support the protocol in the coming months.
The problem: Why we need MCP
Booking flights for an upcoming vacation, pulling analytics data for a weekly sales report, building a prototype of a new feature—these kinds of tasks require context that goes beyond an LLM’s training data and extends to a broad ecosystem of apps and services. The more context an AI assistant has, the better it’s able to understand the specific nuances of a request and deliver a high-quality output.
Consider the scenario of using an AI coding tool to generate code from a design file. If an LLM views a screenshot of the file and uses its training data to interpret the pixels, it might be able to create a rough prototype. But to get to a really useful end product, it needs more context, like the specific variables, components, and styles, or even pseudocode describing the functionality. This kind of context is invaluable for AI, but typically, it lives deep inside other tools.
Before MCP, every developer working on AI agentic tools had to build custom integrations with external apps and services, resulting in slower development and ecosystem fragmentation. With every app exposing data and functions in slightly different ways, each new integration would require a significant amount of upfront work.
Before vs. after: How MCP helps
MCP is a “write once, use anywhere” approach to the problem. An app developer can write a single MCP server for any AI agentic system to use, providing a canonical set of tools and data along with helpful functionality like error handling. Similarly, an AI system can implement the protocol and connect to any MCP server that exists today or in the future.
How MCP works
MCP uses a simple client-server architecture:
1. MCP clients are AI applications like Leap.new or Claude Desktop that request information and execute tasks.
2. MCP servers provide access to external tools, databases, and APIs.
3. The protocol standardizes how they communicate.
When an AI tool needs something external, it sends a request through MCP. The server handles the actual interaction with databases, APIs, or file systems, then sends back the results in a format the AI understands.
For example, if Claude needs to analyze a GitHub pull request, it requests the PR data through MCP. The server fetches it from GitHub's API and returns the structured information that Claude can then analyze and summarize.
The architecture: MCP hosts, clients, and servers
MCP relies on an architecture pattern that’s common in networking, involving hosts, clients, and servers. The protocol defines specific responsibilities for each role.
How MCP works
MCP hosts: Hosts manage the discovery, permissions, and communication between clients and servers. Typically, the host is the product or platform—like Windows OS or Claude Desktop—where users access AI agents to perform tasks. When the model needs access to an external app, the host launches and connects that app’s MCP server and the matching client.
MCP clients: Clients start and maintain a connection to MCP servers, with one client per server. Clients pass requests and responses back and forth between LLMs and MCP servers.
MCP servers: Servers directly plug into external systems (like Figma, Google Drive, or Postgres), providing LLMs with access to data and functionality. MCP servers receive requests from MCP clients and translate them into commands for external apps, like API calls or database queries. They also receive and parse out the app responses into a standard format. Since app developers implement MCP servers, they can control what LLMs get access to, and the protocol provides guidelines around security and permissions.
Real-World use cases
MCP enables many practical applications in development workflows:
1. Code review automation becomes possible when AI can fetch pull request data from GitHub, analyze code changes, and save reviews to Notion or other documentation tools.
2. Database-driven development improves when AI accesses your database schemas and data to generate accurate migrations, queries, and API endpoints that work with your actual data structure.
3. Live system debugging gets smarter when AI queries your application logs, traces, and metrics to identify issues and suggest fixes based on what's actually happening in your system.
These are just a few examples of what becomes possible when AI tools can access external resources through MCP.
Benefits of MCP
Why it matters for AI integration
Developers are adopting MCP because it simplifies connecting LLMs to apps. Compared to custom integrations, MCP has several advantages.
First, developers of AI agentic systems only need to integrate MCP once, and they can then use any MCP server. External application developers only need to create one MCP server, and then any MCP-enabled AI tool can connect to it.
Second, since every MCP server and MCP client must offer the same core interface, switching servers and clients is trivial. That means developers and users can switch between apps like Dropbox and Google Docs, or Slack and Microsoft Teams, with ease.
Finally, as AI products become more context-aware through MCP, AI tools get better for users everywhere. And a standard protocol for the ecosystem means that developers spend less time writing boilerplate integration code, and more time developing new features.
MCP vs. traditional APIs: What's the difference?
Why not use an API instead of MCP? It’s a common question, since APIs provide access to much of the same data and actions in apps. In fact, many MCP servers use APIs behind the scenes to surface data and actions.
The short answer is that MCP lets AI assistants use one set of commands for all APIs, greatly simplifying the integration. Whereas working directly with APIs requires writing custom code for requests, responses, and retries, with MCP, a developer can get the same results by just importing the MCP server. Switching between MCP servers is easy, while switching APIs requires writing a whole new set of code.
MCP is also better optimized for LLM usage. MCP guarantees that everything an LLM needs to access a system is well defined and well documented, in a structured way. Ad hoc API definitions might be missing important descriptions, or make data available in ways that AIs can't understand or might easily misinterpret.
How MCP and AI agents go hand in hand
An AI agent is any AI that takes action in the real world for you. MCP makes it much easier to build AI agents. Instead of integrating APIs one by one, a developer can implement the protocol to let their AI tool take action in any MCP-enabled application.
As the MCP ecosystem grows, agents become more powerful, too. AI agents need to be able to plan and take action across multiple tools in a user’s workspace—the more tools an agent can work with, the more useful it becomes to users. With MCP, agents can easily find and use a growing number of tools for task automation.
MCP vs. A2A: How the protocols complement each other
MCP isn’t the only new LLM protocol around—recently, Google announced the Agent2Agent (A2A) protocol. But developers don’t need to pick one or the other. These protocols solve different problems and actually complement each other.
MCP focuses on enabling AI systems to learn about the world and take actions in it. A2A focuses on helping AI systems communicate with each other about their work and intentions—to collaborate, assign work, argue, delegate, or negotiate.
Two agents might use A2A to decide which one of them is going to do some work, and which one of them is going to supervise the work. Then, they might use MCP for giving each other instructions on work to be done and data to use.
The future of MCP and AI integration
The MCP team continually updates the protocol, with new releases shipping multiple times a month. MCP is also an open standard, so the larger AI community can contribute to the roadmap. We can expect to see more of a focus on security in the coming releases: authentication, authorization, and data filtering or privacy mechanisms for specific domains (like healthcare or finance).
If MCP grows to see wide adoption, then entire portions of the AI industry may begin optimizing their use of MCP. Model developers might start to include MCP tool usage in their training data, and LLM orchestration frameworks (e.g., LangChain) may support MCP as a first-class citizen in their use cases.
Over time, MCP could become the de facto standard for connecting AI to the entire digital landscape of tools and services. Bringing data and actions from external systems into LLM experiences will become increasingly plug-and-play in the AI ecosystem. For developers, that translates into fewer bespoke wrappers, better AI tools, and more time to build the features users really value. For companies shipping data-rich apps, it means instant compatibility with every MCP-enabled AI platform. And for end users, it unlocks better AI applications and personal assistants that can finally fetch the right context, push the right buttons, and get real work done without tedious intervention.
