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 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 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.
