Understanding MCP: A Client-Server Architecture
The realm of Large Language Models (LLMs) often necessitates connecting them to external resources like data repositories, specialized tools, or Application Programming Interfaces (APIs) to unlock their true potential. However, a standardized method for this integration has been conspicuously absent – until now.
Anthropic has stepped forward with a potential solution: the Model Context Protocol (MCP), an open protocol aiming to become the ‘USB-C’ of the AI world. MCP is an open-source project spearheaded by the creators of the Claude model. It envisions a universal, open standard that seamlessly connects AI systems with various data sources.
MCP operates on a conventional client-server architecture, composed of three fundamental elements: the host, the client, and the server.
The host is typically a user-facing interface, such as Claude Desktop or an Integrated Development Environment (IDE) like Cursor. It is responsible for managing one or more MCP clients. The host provides the interface through which users interact with the LLM and indirectly with the external resources connected via MCP. It handles user input, presents information, and orchestrates the overall interaction flow. The host’s role is crucial in providing a seamless and intuitive user experience, abstracting away the complexities of the underlying MCP architecture. The host often provides configuration options to manage MCP clients and servers, allowing users to customize the integration and tailor it to their specific needs.
Each client establishes a dedicated connection with the server via the MCP protocol. Communication between client and server occurs through JSON-RPC messages, with the transport layer varying based on the specific implementation. Currently supported transport layers include Stdio, HTTP, and server-sent events (SSE). The client acts as an intermediary between the host and the server, translating requests from the host into MCP-compliant messages and forwarding them to the appropriate server. It also receives responses from the server and relays them back to the host. The client handles the complexities of the MCP protocol, ensuring that communication between the host and the server is reliable and efficient. Different client implementations may offer varying levels of performance and security, depending on the specific transport layer and security protocols used. The modular design of MCP allows for different client implementations to be used interchangeably, providing flexibility and allowing developers to choose the best client for their specific use case.
The MCP server exposes specific capabilities to the client, making them accessible to the host in a standardized manner. This uniform accessibility is the core reason why MCP is often compared to USB-C for AI. The server is the gateway to external resources, providing a standardized interface for accessing data, tools, and APIs. It handles the actual interaction with the underlying resource, translating MCP requests into resource-specific commands and returning results in a consistent format. The server’s role is crucial in abstracting away the complexities of different resource interfaces, allowing LLMs to interact with them seamlessly. The consistent exposure and response mechanism is a key aspect of MCP, ensuring uniformity across different resources. This uniformity simplifies the integration process and reduces the cognitive load on developers, who only need to learn a single protocol to interact with a wide range of resources. The server can be implemented in various programming languages and deployed in different environments, providing flexibility and allowing developers to choose the best technology stack for their specific needs.
Much like USB revolutionized connectivity by eliminating the need for disparate interfaces for peripherals and storage devices, MCP strives to create a common language for models to interact with data and tools. USB achieved interoperability by defining a standard set of protocols and interfaces that all devices could adhere to. Similarly, MCP aims to achieve interoperability between LLMs and external resources by providing a standardized protocol for communication and data exchange. This standardization reduces the friction associated with integrating LLMs with different resources and allows developers to focus on building innovative applications.
The MCP server’s role depends on the resource’s location. For local resources, such as a SQLite database, the server directly accesses the resource. For remote resources, like an S3 bucket, it acts as a bridge, relaying API calls. This bridging function reinforces the USB-C analogy, as MCP servers often serve as adapters, translating vendor-specific interfaces into a standardized format that language models can easily understand. The server handles the complexities of authentication, authorization, and data formatting, ensuring that the LLM can access the resource securely and efficiently. The bridging function also allows LLMs to interact with resources that are behind firewalls or require specific network configurations.
The consistent exposure and response mechanism is a key aspect of MCP, ensuring uniformity across different resources. The server exposes a consistent set of methods and data structures, regardless of the underlying resource. This uniformity simplifies the integration process and reduces the amount of code that developers need to write. The server also handles error handling and logging, providing valuable information for debugging and troubleshooting.
One of MCP’s most intriguing features is its bi-directional communication capability. Not only can the host application request data from the server, but the server can also communicate with the LLM through sampling/createMessage requests to the client. While this functionality is not yet universally supported, it paves the way for exciting agentic workflows. This bi-directional communication allows for more dynamic and interactive applications, where the LLM can respond to events and data changes in real-time. For example, a server could notify the LLM when a new email arrives or when a sensor reading exceeds a certain threshold. This capability opens up new possibilities for building intelligent agents that can proactively monitor and respond to their environment.
With a foundational understanding of MCP, let’s examine its practical application.
Hands-On with MCP: Testing with Claude Desktop
Given Anthropic’s development of MCP, Claude Desktop provides a straightforward environment for initial experimentation. Claude Desktop offers a user-friendly interface for interacting with Claude and exploring its capabilities. Its integration with MCP makes it an ideal platform for testing and experimenting with MCP servers.
For users who prefer to avoid third-party LLM providers, the subsequent section will cover connecting MCP servers to local models and the Open WebUI interface. Using local models provides more control over data privacy and security, as the data is processed locally and not sent to a third-party server. Open WebUI provides a web-based interface for interacting with local LLMs and MCP servers, making it easy to deploy and manage them.
Beyond Claude Desktop, a few dependencies are needed as MCP servers can operate in various environments. For this demonstration, Node.js, Python 3, and the UVX package manager for Python must be installed. Node.js is a JavaScript runtime environment that is commonly used for building server-side applications. Python 3 is a versatile programming language that is widely used for data science and machine learning. UVX is a package manager for Python that simplifies the process of installing and managing Python packages.
After installing the necessary dependencies, launch Claude Desktop and log in using an Anthropic account. Navigate to the application settings and then to the ‘Developer’ tab. The developer tab provides access to advanced settings and features, including the MCP client configuration.
Clicking the ‘Edit Config’ button will automatically generate an empty claude_desktop_config.json file in the ~/Library/Application Support/Claude/ folder on macOS or the %APPDATA%\\Claude\\ folder on Windows. This file will house the MCP Client configuration. The claude_desktop_config.json file is a JSON file that contains the configuration settings for the MCP client. It specifies the servers that the client should connect to and the capabilities that it should expose to Claude.
The System Time and File System MCP servers will be used for testing purposes. The System Time server provides access to the system’s current time and date. The File System server provides access to the file system, allowing Claude to read and write files. These servers are relatively simple and easy to set up, making them ideal for initial experimentation.
Open the claude_desktop_config.json file in a text editor or IDE (e.g., VSCodium) and replace its contents with the following time-server configuration, adjusting the time zone as desired: