Claude can finally perform web searches, access local files, and databases! What breakthrough technology enables this? This article thoroughly examines MCP (Model Context Protocol)—its working principles, core functionalities, and real-world applications—to help you fully grasp this revolutionary technology dubbed the "USB interface for AI."

Basic Concepts and Background of MCP

What is MCP?

MCP (Model Context Protocol) is an open-source communication protocol released by Anthropic in November 2024, designed to facilitate seamless integration between large language models (LLMs) and external data sources or tools. By standardizing interactions between AI systems and data sources, MCP enables models to access richer contextual information and generate more accurate, relevant responses.

Simply put, MCP acts as a "universal interface" for AI, allowing standardized bidirectional communication between AI models and various external systems and data sources. Much like USB-C provides a standardized way to connect devices, MCP offers a unified method for linking AI models to diverse data sources.

Development Background of MCP

Before MCP, even the most advanced AI models faced limitations due to data isolation. Each new data source required custom implementation, increasing development costs and creating inefficiencies while making systems difficult to scale.

Anthropic recognized that as AI assistants gained mainstream adoption, the industry invested heavily in model capabilities, yet even the most sophisticated models remained constrained by data isolation. MCP was developed to address this challenge, allowing developers to establish secure bidirectional connections between data sources and AI tools.

Core Architecture and Working Principles of MCP

Client-Server Architecture

MCP Architecture Diagram

MCP employs a classic client-server architecture:

MCP Host: Typically the LLM application initiating the connection, such as Claude Desktop or other AI tools. It manages the connection between the MCP Client and Server.
MCP Client: Resides within the host application and maintains a 1:1 connection with the server, handling protocol communication. It facilitates interaction between the AI and MCP Server.
MCP Server: A lightweight program responsible for exposing specific data sources or tool functionalities and interacting with the client via standardized protocols. It manages instructions for local database outputs, allowing the Client to select and execute operations autonomously.

Communication Flow

MCP communication is based on JSON-RPC 2.0, supporting three message types—requests, responses, and notifications—to ensure standardized and consistent interactions.

The workflow is as follows:

A user sends a request via an AI application.
The AI application (host) relays the request to the MCP Server via the MCP Client.
The MCP Server processes the request, accessing the relevant data source or executing the tool function.
The Server returns results to the Client.
The Client passes the information to the AI model.
The AI model generates a response based on this information.

Four Core Functionalities of MCP

MCP's Four Core Functionalities

MCP provides four core primitives (server-side primitives) to standardize interactions between clients and servers:

1. Resources

Resources represent any type of data an MCP Server wishes to provide to the Client, including:

File contents
Database records
API responses
Real-time system data
Screenshots and images
Log files

Each resource is identified by a unique URI and can contain text or binary data.

2. Prompts

Prompts in MCP are predefined templates that can:

Accept dynamic parameters
Incorporate context
Chain multiple interactions
Guide specific workflows
Surface as UI elements (e.g., slash commands)

3. Tools

MCP Tools allow Servers to expose executable functions that Clients can invoke. Key aspects include:

Discovery (tools/list): Clients can list available tools.
Invocation (tools/call): Servers execute requested operations and return results.
Flexibility: Tools range from simple calculations to complex API interactions.

4. Sampling

Sampling is a powerful MCP feature that enables Servers to request LLM completions through Clients, facilitating complex agent behaviors while maintaining security and privacy. This human-in-the-loop design ensures users retain control over what the LLM sees and generates.

How MCP Expands Claude AI's Capabilities

Breaking Model Limitations

Before MCP, Claude and other AI models faced inherent constraints:

Inability to access real-time data
No direct execution of computations or code
Limited interaction with external systems and services

MCP overcomes these limitations by providing standardized interfaces, enabling Claude AI to:

Access up-to-date web data and information
Perform complex calculations and data analysis
Invoke specialized tools and services
Seamlessly integrate with enterprise systems

Practical Enhancements for Claude

MCP allows Claude AI to dynamically connect to external tools and data sources, significantly expanding its problem-solving capabilities. For example, with MCP, Claude AI can now:

Query the latest web information for timely responses
Analyze user-uploaded documents and data
Execute code and return results
Integrate with internal enterprise systems for customized business support

Real-World Applications of MCP

1. Internet Search Integration

Via MCP, Claude can connect to search engine APIs like Brave Search for real-time web searches.

Configuration example:

"mcpServers": {
  "brave-search": {
    "command": "npx",
    "args": [
      "-y",
      "@modelcontextprotocol/server-brave-search"
    ],
    "env": {
      "BRAVE_API_KEY": "YOUR_API_KEY"
    }
  }
}

This enables Claude to answer queries about recent events, real-time data, or web-based information.

2. Database Access

MCP allows Claude to connect to local or remote databases like SQLite or PostgreSQL.

Configuration example:

"mcpServers": {
  "sqlite": {
    "command": "uvx",
    "args": ["mcp-server-sqlite", "--db-path", "/Users/YOUR_USERNAME/test.db"]
  }
}

This lets Claude perform data queries, analysis, and management tasks, translating natural language into SQL queries.

3. Filesystem Integration

MCP enables Claude to access specified folders in a user's local filesystem.

Configuration example:

"mcpServers": {
  "filesystem": {
    "command": "npx",
    "args": [
      "-y",
      "@modelcontextprotocol/server-filesystem",
      "/Users/YOUR_USERNAME/Desktop"
    ]
  }
}

This allows Claude to read, analyze, create, or modify files.

4. Web Scraping

MCP empowers Claude to scrape and analyze webpage content. Given a URL, Claude can extract content for translation, summarization, or other processing.

5. Creative Application Development

Developers have demonstrated using MCP to enable Claude to create fully functional drawing applications. Pietro Schirano's prototype shows how easily visual and interactive tools can be built with AI, with Claude+MCP matching the capabilities of tools like Cursor.

Getting Started with MCP

Claude Desktop Setup Guide

Install Required Software:
- Claude desktop application
- Node.js (v20.16.0 or later)
- Python (3.10 or later)
- uv and other dependencies
Configure Claude:
- Locate or create Claude's config file: /Library/Application Support/Claude/claude_desktop_config.json
- Add desired MCP Server configurations
- Restart Claude for changes to take effect
Enable Developer Mode:
- Open Claude Desktop
- Click "Claude" in the menu bar
- Select "Settings"
- Under "Developer," check "Enable Developer Mode"

Recommended MCP Servers

Beyond those mentioned, other MCP Servers include:

Google Drive Server: Search Google Drive data
Slack Server: Integrate Slack channel management and messaging
Memory Server: Persistent memory system for knowledge graphs
Google Maps Server: Location services, routes, and place details
Fetch Server: Web content retrieval and processing

Developing Custom MCP Servers

Developers can create custom MCP Servers for specific needs. Official SDKs and examples in Python and TypeScript are available for reference.

Advantages and Future of MCP

Core Advantages of MCP

Standardization: MCP provides a unified protocol, reducing the need for custom connectors per data source.
Flexibility: Enables AI applications to connect to diverse data sources and tools.
Security: Ensures encrypted data transfer with strict permission controls and configurable access.
Openness: As an open protocol, MCP allows any developer to create Servers for their products.

Potential Impact and Challenges

MCP has the potential to become the "HTTP of AI," standardizing and decentralizing LLM applications. As the ecosystem matures, AI systems will maintain context across tools and datasets, replacing fragmented integrations with sustainable architectures.

Conclusion

MCP represents a major breakthrough in AI integration, empowering models like Claude to interact with the external world. It simplifies development while enhancing security and scalability, enabling AI to better integrate into workflows and applications.

As more developers and enterprises adopt MCP, we can expect innovative AI applications and services to emerge, further advancing AI technology. MCP is not just a technical protocol—it's a significant step toward a more open and connected future for AI.

What is MCP?

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