Model Context Protocol (MCP)

The Model Context Protocol (MCP) is an open standard introduced by Anthropic that standardizes how AI applications connect with external tools, data sources, and systems. It enables AI assistants to access and interact with data where it lives, breaking down information silos.

Overview of MCP connectivity

Unlike proprietary integration methods, MCP is designed as a collaborative, open-source ecosystem that any developer or organization can contribute to and benefit from.

MCP follows a client-server architecture with three primary components working together to enable AI models to access external data and capabilities.

General MCP architecture showing the relationship between components

Animated visualization of MCP data flow

The architecture is designed to be modular, allowing different components to be developed and maintained independently. This modularity enables the ecosystem to grow organically, with developers creating new MCP servers for various data sources and services.

Importantly, MCP is designed with security in mind, ensuring that sensitive data remains within your security boundaries while still being accessible to AI models.

MCP operates through a structured workflow that enables AI models to request information from various data sources and receive responses in a standardized format.

MCP workflow showing interaction between components

Step-by-Step Process

Example of MCP in action with Claude

The standardized nature of MCP means that AI models don't need to understand the specific details of each data source — they only need to know how to interact with the MCP protocol itself.

MCP implementation can take various forms depending on the use case. Below are examples of how MCP can be implemented in different scenarios.

Detailed example of MCP implementation flow

from mcp.server import MCPServer
from mcp.schema import Capability, Action

# Create a simple MCP server
server = MCPServer(name="FileReader",
                   description="Reads files from the local filesystem")

# Define a capability
file_capability = Capability(
    name="read_file",
    description="Reads content from a file",
    parameters={
        "path": {
            "type": "string",
            "description": "Path to the file"
        }
    },
    returns={
        "content": {
            "type": "string",
            "description": "Content of the file"
        }
    }
)

# Implement the capability
@server.implement(file_capability)
def read_file(path):
    try:
        with open(path, 'r') as f:
            content = f.read()
        return {"content": content}
    except Exception as e:
        return {"error": str(e)}

# Start the server
if __name__ == "__main__":
    server.start()
                    

Claude Desktop MCP implementation

MCP offers numerous benefits for developers, organizations, and end-users, making it a powerful standard for AI integration.

How MCP enhances AI capabilities and security

MCP enables a wide range of powerful use cases across different industries and scenarios. Here are some examples of how MCP is being used in real-world applications.

Various MCP applications across industries

Real-World Success Stories

Block

"Open technologies like the Model Context Protocol are the bridges that connect AI to real-world applications, ensuring innovation is accessible, transparent, and rooted in collaboration."

- Dhanji R. Prasanna, Chief Technology Officer at Block

Apollo

Apollo has integrated MCP into their systems to enhance their platform capabilities and provide better AI-powered features to their users.

Development Tool Companies

Companies including Zed, Replit, Codeium, and Sourcegraph are working with MCP to enhance their platforms—enabling AI agents to better retrieve relevant information.