What is MCP?

Claude is an advanced AI model developed by Anthropic that helps users tackle everything from answering complex questions to generating code. But like all AI systems, Claude faces limitations based on its pre-trained knowledge and built-in tools. To break through these barriers while addressing enterprise data security concerns, Anthropic created the Model Context Protocol (MCP) – a game-changing standard that lets AI models like Claude seamlessly connect with external tools, data sources, and development environments. In this article, we’ll dive into what MCP is, why it was developed, how it works, and what makes it special.

What is Claude MCP?

Claude MCP (Model Context Protocol) is an open protocol standard created by Anthropic that bridges the gap between AI models and the outside world. Think of it as a universal translator that helps AI better understand and process code while interacting with various data sources and tools. MCP gives AI systems a standardized way to connect to external systems, pull in real-time data, and execute tasks – making AI assistants dramatically more powerful and versatile.

In simple terms, MCP works like a “USB-C interface” for AI, allowing it to “plug into” virtually any data source or tool – from APIs and databases to enterprise applications – using a standardized approach. This eliminates the need for custom integrations with each new data source, making AI solutions more flexible and practical for real-world use.

Why Was MCP Developed?

MCP was born from a fundamental challenge: AI assistants typically work in isolation from real-time data and external systems. Before MCP, connecting an AI model to different data sources meant building custom integrations for each one – a major headache for developers that was both time-consuming and inefficient. For instance, getting an AI to access weather data or tap into a company’s CRM system required writing specialized code to bridge these systems.

MCP solves this problem by providing a universal open standard. With this protocol in place, MCP servers written by users can easily work with any LLM, eliminating the need to reinvent the wheel for each integration.

How MCP Works

MCP uses a client-server architecture where the AI model (like Claude Desktop) acts as the client, while various data sources or tools function as MCP servers. The communication between client and server runs on the JSON-RPC 2.0 protocol, transmitted through HTTP along with Server-Sent Events (SSE – though this is gradually being replaced by Stream HTTP) or standard I/O streams for real-time interaction.

The MCP Workflow

1. Request Phase: The AI (acting as an MCP client) sends a JSON request to the MCP server specifying what it needs. For example, Claude might ask a weather MCP server for current conditions in Chicago.
2. Processing Phase: The MCP server handles the request by calling external APIs, querying databases, or performing calculations. In our weather example, the server would reach out to a weather API for the latest data.
3. Response Phase: The server packages the results in JSON format and sends them back to the AI, which then uses this information to generate responses or take additional actions.

Key MCP Components

• Host: This is the application (like Claude Desktop or an IDE) that contains the large language model and initiates connections.

• MCP Client: Living inside the host application, the client maintains a direct connection to the server.

• MCP Server: The server delivers Resources, Tools, and Prompts to the client as needed.

• Resources: These provide content and data to the LLM through the MCP Server.

• Prompts: These are templates or workflows that guide how the LLM responds.

• Tools: These give the LLM access to external capabilities and functions.

• Sampling: This allows the MCP Server to actively request completion results from the LLM.

• Roots: These define the operational boundaries in the MCP protocol, giving clients a standardized way to identify resources.

• Transports: These are the communication channels, currently including Stdio, SSE (not recommended), and Stream HTTP.

MCP’s Standout Features

MCP’s design prioritizes standardization, security, and flexibility, with four key advantages:

• Universal Integration: MCP offers a common interface for AI models to interact with virtually any API or data source in a “plug-and-play” fashion. No more custom integration work for each new data source, dramatically cutting development time and complexity.
• Rock-Solid Security: With built-in encryption, access controls, and user approval mechanisms, MCP ensures that AI operations remain secure and controllable. It also supports self-hosting sensitive data, enhancing privacy protection.
• Complete Flexibility: MCP works with any AI model – Claude, GPT-4, Llama, Coze, you name it. Being open-source, it welcomes community contributions, extensions, and adaptations.
• Real-World Applications: MCP shines in scenarios needing real-time data access, such as enterprise knowledge management, DevOps workflows, and integrations with CRM or financial services.

The Bottom Line

MCP represents a major milestone in AI development by providing a standardized way for AI models to connect with the external world. As a pioneering technology from Anthropic, MCP solves the isolation problem that’s long plagued traditional AI assistants. This protocol delivers four game-changing benefits:

1. Freedom from Isolation: AI can now access real-time data and external tools, no longer limited to what it learned during training.
2. Developer Efficiency: No need to build custom integrations for each data source, saving countless hours of development time.
3. Enterprise-Grade Security: Built-in encryption and access controls protect sensitive information.
4. Model Flexibility: As an open standard, MCP works with virtually any AI model, not just Claude.

As businesses increasingly seek powerful AI solutions, MCP’s value becomes even more apparent. It enables AI to securely connect to enterprise knowledge bases, development tools, and business applications while maintaining strict data privacy. MCP isn’t just a technical protocol – it’s a bridge between AI and real-world systems that paves the way for more intelligent, practical applications.

For companies looking to boost operational efficiency with AI, understanding and implementing MCP will be a key competitive advantage. And as an open standard, we can expect to see an explosion of innovations and applications built around MCP in the coming years.