AI assistants are drafting legal briefs, debugging code, writing emails, and helping manage workflows across companies. But even the most capable models like Claude, GPT-4, or Gemini struggle with one thing: actions!
Most AI assistants operate in silos. They can generate great output but lack access to the tools and data that businesses rely on—like Google Drive, GitHub, Slack or internal databases and tools.
As organizations push toward more integrated AI systems, they’re hitting the same wall: how do you efficiently connect AI to the growing sprawl of tools, data sources, and platforms?
Anthropic’s Model Context Protocol (MCP) proposes a solution. Think of it as the USB-C of the AI world: one standard that any AI application can use to connect with any external system—securely, consistently, and without starting from scratch every time.
This blog unpacks what MCP is, how it works, and why it's becoming one of the most important developments in AI infrastructure while looking at who's adopting it, what it unlocks for developers and organizations, and how it could shape the future of AI agents.
The problem: AI models are powerful but isolated
Most AI models are trained on static datasets and lack live access to the systems companies use every day.
The expansion of Large Language Models (LLMs) and sophisticated AI assistants is rapidly transforming various aspects of our digital lives, from content creation to software development.
As these AI models become increasingly integrated into our workflows, a significant challenge has emerged: how to effectively connect these intelligent systems with the vast and diverse ecosystem of data sources, applications, and tools that underpin our modern world.
Even the most advanced AI models are often limited by their isolation, existing in a digital realm largely disconnected from the real-time information and functionalities of external systems.
The solution: Anthropic’s Model Context Protocol
Addressing this fundamental hurdle, Anthropic has introduced the Model Context Protocol (MCP), an open standard poised to revolutionize how AI interacts with the world around it, offering a standardized solution to previously complex integration challenges.
At its core, the Model Context Protocol (MCP) is an open protocol designed to standardize the way applications provide contextual information to LLMs.
This can be intuitively understood by drawing an analogy to a USB-C port, which serves as a universal connector for various computer peripherals and accessories; MCP aims to be the universal connector for AI applications and diverse data sources.
The primary objective of MCP is to enable frontier AI models to generate better and more relevant responses by granting them access to the necessary data and tools.
How it works: hosts, clients & servers
MCP employs a client-server architecture. In this model, MCP Hosts are AI applications, such as Anthropic's Claude Desktop or plugins for Integrated Development Environments (IDEs), that initiate connections. MCP Clients maintain 1:1 connections with servers, inside the host application.
Conversely, MCP Servers are programs that expose data, tools, and predefined prompts, providing specialized context and capabilities to the clients.
A Host process acts as a container and coordinator, managing multiple client instances, their permissions, and overall security. The communication between clients and servers is governed by standardized message types including Requests, Results, Errors, and Notifications. Servers can provide capabilities like access to resources and tools, while clients maintain connections with servers through the protocol.
Why it matters: simpler integration and broader adoption
The Model Context Protocol has rapidly gained traction and become a prominent topic within the AI and development communities.
Several factors contribute to this growing momentum:
- Firstly, the protocol is backed by Anthropic, a significant player in the AI landscape, lending credibility and ensuring ongoing development and support. (Recently both OpenAI and Google have been using this too)
- Secondly, MCP is an open standard, accompanied by comprehensive documentation, which fosters transparency and encourages broad adoption.
- Furthermore, its launch was comprehensive, including Software Development Kits (SDKs) for popular programming languages and reference implementations, enabling developers to start building immediately.
This has led to early adoption by prominent development tools companies such as Zed, Replit, Codeium, and Sourcegraph, which are integrating MCP to enhance their AI-powered features. The availability of pre-built MCP servers for widely used enterprise systems like Google Drive, Slack, GitHub, and Postgres further lowers the barrier to entry and accelerates adoption.
The emergence of platforms like Smithery and Glama, which serve as marketplaces for discovering and listing MCP servers, indicates a burgeoning ecosystem around the protocol.
Real-world use: from software to personal productivity
The practical benefits of MCP are already being realized across a multitude of domains. In the realm of enterprise data assistants, MCP enables the creation of AI assistants that can securely access and process data from various internal systems.
For instance, a corporate chatbot can leverage MCP to retrieve employee HR records from a database, check project details stored in a project management tool, and even post updates to a team's Slack channel, all within a single, standardized interaction.
Within software development and coding: MCP is being integrated into coding assistants like Sourcegraph Cody, Zed Editor, and Replit. These tools can now fetch code context, and relevant documentation and even execute actions within code repositories, providing developers with more accurate and context-aware assistance.
An IDE equipped with MCP: could allow an AI to read project files, execute build and test commands, or search through version history based on a developer's query. Sourcegraph Cody, for example, uses MCP to access extensive codebases and documentation, offering developers more precise code suggestions. Zed Editor has also incorporated MCP to allow its AI features to interact seamlessly with various development tools and resources.
For personal productivity: MCP can power personal AI agents that can manage tasks across different applications.
Imagine a virtual assistant that can read your email, add events to your calendar, and update your to-do list, all through standardized MCP servers for each application.
A community-developed "Gmail agent" demonstrates the potential, capable of reading and drafting emails using a Gmail connector built on MCP.
Automation tools also benefit from MCP. For example, an MCP server for Puppeteer allows AI models to interact with and automate web browsers for tasks like web scraping. This enables scenarios like extending Claude Desktop to use Puppeteer for browser automation and web scraping via Docker.
Furthermore, MCP facilitates seamless database interaction, with servers allowing AI to query and manipulate databases like PostgreSQL and SQLite.
A coding assistant could use an MCP server to run SQL queries on a local database to fetch test data or configurations. The utility of MCP extends to cloud platform management, with servers being developed to interact with services like Cloudflare and Kubernetes.
Architecture: hosts, clients & servers
The underlying architecture of MCP revolves around the client-server model, comprising three main components: the Host, the Client, and the Server.
The Host is the main AI application that manages the interaction. The Client acts as an intermediary, handling communication with a specific Server through request-response patterns and notifications. The Server provides access to resources, tools, and prompts.
Communication between these components is governed by a set of core message types including Requests, Results, Errors, and Notifications exchanged through JSON-RPC 2.0 formatting.
MCP supports various transport mechanisms for communication, such as Stdio for local processes and HTTP with Server-Sent Events (SSE) for more distributed scenarios.
The protocol defines standard error codes and error handling mechanisms to ensure robust communication between components.
Security: Fine-grained control and local-first deployments
Security is a paramount consideration in the design of MCP, with a strong emphasis on controlled access for AI models. The Host application plays a crucial role in instantiating clients and explicitly approving connections to servers, giving users granular control over what an AI assistant can access.
Each MCP server requires explicit permission to operate, and the tools it exposes run with only the privileges granted to them. Initially, MCP deployments have focused on a local-first approach, enhancing security by keeping connections within the user's own machine or network.
While the vision includes support for remote and cloud-based connections, future iterations will incorporate added layers of authentication and security to maintain this level of control in distributed environments.
Developers implementing MCP are also advised to consider standard security practices such as robust authorization and authentication mechanisms, secure handling of tokens, fine-grained data access controls, and ensuring transport security through HTTPS.
Future impact
Looking ahead, the Model Context Protocol could play a key role in reshaping how AI systems are built and connected.
As more tools and models adopt MCP, we could see a much more standardized environment—where different AI models and services work together smoothly. This would make it easier for organizations to try out different AI providers without having to rebuild integrations from scratch.
The network effect of broad adoption could speed up how quickly AI gets rolled out across industries. MCP also creates a solid base for more autonomous, context-aware AI agents that can take on complex tasks with less human input.
We might even see things like “MCP docs” or “MCP endpoints” become as common as API documentation. And because it supports easy connections between different data sources, MCP opens new doors for better collaboration and knowledge sharing across teams and systems.
The official MCP roadmap for early 2025 outlines several key priorities: improving remote MCP connections with enhanced authentication and service discovery; creating comprehensive reference implementations; developing better distribution and discovery mechanisms including package management and server registries; expanding agent support for complex workflows; and fostering a broader ecosystem through community-led standards development and support for additional modalities beyond text. The MCP team welcomes community participation in shaping these future directions.
Conclusion
In conclusion, Anthropic’s Model Context Protocol is a major step forward for AI integration. It offers a clear, open standard for connecting AI models to the tools and data they need—solving long-standing issues around complexity and isolation.
MCP has the potential to simplify development, improve compatibility across AI systems, and support more capable, context-aware AI agents.
As more developers and organizations adopt the protocol, the ecosystem around it is quickly expanding. MCP marks an important shift toward AI systems that are not only more connected, but also more useful, flexible, and intelligent.