Understanding the Model Context Protocol (MCP)

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Analysis of the historical development background of Model Context Protocol (MCP)

     At the beginning of the 21st century, with the rise of deep learning technology, large language models (LLMs) gradually became the core of AI research and application. However, early models (such as the early GPT series) mainly relied on static training data, and their capabilities were limited by the knowledge boundaries during training, and they could not directly obtain real-time data or interact with external systems. This "isolated" feature exposed a series of problems in practical applications: the model could not understand the user's historical context, could not call external tools to perform tasks, and could not dynamically update the knowledge base.

     As AI application scenarios become more complex, such as multi-round dialogue systems, code generation tools, and enterprise-level data analysis, developers are beginning to try to connect models to external data sources through customized APIs or plug-ins. However, this approach brings significant integration challenges. Each data source (such as Google Drive, Slack, or internal database) requires independent interface development, resulting in duplication of work and a surge in maintenance costs. This "point-to-point" integration of the NxM problem (N models docking with M data sources) limits system scalability and inefficient development, while also increasing the difficulty of security and consistency management.

     In the 2020s, the development of AI has entered a new stage, and the industry has begun to focus on how to make the model shift from "passive answering" to "active execution". This shift has given rise to the need for standardized protocols, similar to HTTP or Language Server Protocol (LSP) in software engineering. The success of LSP provides a paradigm for development tools: through a unified protocol, the integration between editors and programming language support is simplified from an NxM problem to an M+N problem, greatly improving the collaborative efficiency of the ecosystem. The AI ​​field also needs similar solutions to break down data silos and simplify the integration process.

     At the same time, open source communities and enterprises have put forward higher requirements for the interoperability of the AI ​​ecosystem. Platforms such as Hugging Face have promoted model sharing, while frameworks such as LangChain have attempted to enhance model capabilities through tool calling. However, these solutions still do not solve the fundamental problem: the lack of a universal and standardized context transfer mechanism. The industry has begun to realize that without a unified protocol, the potential of AI agents will be difficult to fully unleash.

     It is in this context that the Model Context Protocol (MCP) was officially launched and open-sourced by Anthropic in November 2024. As a company founded by former OpenAI researchers, Anthropic is known for its expertise in explainable and secure AI systems.

     The original intention of MCP’s design was to create an open protocol to standardize the way AI models interact with external data sources and tools, thereby solving the fragmentation problem of traditional integration.

How to understand the "Model Context Protocol (MCP)"?

      First, let’s take a more technical look at this…

     The core design of the Model Context Protocol (MCP) follows the client-server architecture, which allows a host application to establish connections with multiple servers, thereby achieving flexible context transfer and function extension.

    Generally speaking, the technical framework of MCP is built around three key components: host, client, and server. These components work together to form an efficient and scalable ecosystem, providing a solid foundation for the dynamic interaction between AI models and external resources. Before we delve into the technical details, let's briefly review the roles and functions of these three components to help readers establish a clear cognitive framework and lay the foundation for subsequent in-depth discussions.

     In the architecture of the Model Context Protocol (MCP), the host refers to any application that can host an AI interactive environment, such as mainstream AI tools such as Claude Desktop and Cursor. These hosts not only provide users with a platform for interacting with artificial intelligence models, but are also responsible for integrating external tools, accessing diverse data resources, and running the MCP Client to implement the core functions of the protocol. As the cornerstone of the entire system, the host ensures that AI models can seamlessly call external capabilities by providing a dynamic and scalable operating environment, thereby improving their practicality and intelligence.

     The MCP Client is a key component running inside the host, responsible for establishing efficient communication with the MCP Server. It acts as a bridge between the host and external resources, coordinating data transmission and command interaction through standardized protocol interfaces to ensure the real-time and consistency of information.

     The design of the MCP client fully embodies the concepts of modularity and lightweight, enabling the host application to flexibly connect to multiple servers, thereby supporting the execution of complex tasks such as multi-source data integration or cross-tool collaboration.

     For specific interactions, please refer to the following:

     Finally, in the Model Context Protocol (MCP) system, the MCP Server plays a vital role, injecting strong support into the entire ecosystem by exposing specific functional interfaces and data access capabilities.

     The MCP server not only connects external resources and AI models, but also provides diversified services in a standardized way to meet the needs of complex application scenarios. Specifically, its functions can be divided into the following key aspects:

      1. Tools

     The MCP server provides the ability to perform specific operations for large language models (LLMs). For example, through the tool interface on the server side, LLMs can complete various tasks from code debugging to file management, thereby transforming the model's language generation capabilities into actual productivity.

      2. Resources

      The server is responsible for exposing content and information from different data sources to LLMs, such as internal enterprise databases, cloud storage files, or real-time API data. This openness of resources gives the model stronger context awareness, enabling it to generate more accurate output based on the latest data.

      3. Prompts

     The MCP server supports the creation of reusable prompt templates and workflows to help developers design standardized interaction modes. This feature is particularly suitable for task scenarios that require efficient iteration or batch processing, such as automated customer service or content generation processes.

     It is worth emphasizing that understanding the communication mechanism between the client and the server is the core prerequisite for developing a custom MCP client-server system. The client-server architecture of MCP is based on standardized protocols (such as JSON-RPC), which enables efficient data exchange and function calls through a well-defined request-response model.

     Therefore, in actual business scenarios, for developers who want to build personalized MCP solutions, in-depth understanding of this communication process will not only help optimize system performance, but also unlock more innovative application possibilities, such as multi-server collaboration or cross-platform integration. In other words, the MCP server is not only a provider of functions, but also a link between AI agents and the real world, and the client-server collaborative design is the cornerstone for the vigorous development of this ecosystem.

How does the Model Context Protocol (MCP) work?

     The MCP client or host is the starting point of the entire interaction process, responsible for initiating requests and connecting AI models with external resources. Typical application scenarios of the client include the following:

•      AI models: Large language models (LLMs) such as Claude and GPT, which require external tools to enhance their functionality, such as performing tasks or obtaining real-time data.

•      Applications: such as Claude Desktop, code editors (such as Cursor or VS Code), etc. These tools provide users with an operation interface and integrate AI capabilities through MCP.

•      Any connected system: Any system designed to interface an AI model with external resources (such as a database, API, or file system) can act as an MCP client.

     The client sends a request to the server through the MCP protocol to access tools or obtain information. This process is similar to the mechanism by which a web browser requests page content from a server: the user (or AI) makes a request, the client converts it into a standardized request, and waits for the server's response. This design ensures the flexibility and versatility of the request, laying the foundation for subsequent resource calls.

     2. Communication layer: the core link of standard protocols

    The communication layer of MCP is the core of the entire system. It coordinates the interaction between the client and the server by defining a standard protocol (The Protocol). This protocol is not only the cornerstone of technical implementation, but also the key to MCP's cross-model and cross-tool compatibility. Its main functions include:

•      Format definition: Establish a unified structure for requests and responses (such as a data format based on JSON-RPC) to ensure that both communicating parties can accurately parse each other's information.

•      Compatibility guarantee: Through standardized interfaces, different AI models (such as Claude and LLaMA) can work seamlessly with various tools, eliminating barriers between heterogeneous systems.

•      Security and robustness: Built-in security mechanisms (such as authentication and encryption) and error handling logic, as well as standardized data formats, ensure the stability and reliability of communications.

     This standard protocol functions similarly to HTTP on the Internet. It provides a common "language" for all participants in the MCP ecosystem, allowing all parts of the system to collaborate smoothly regardless of the AI ​​model or external resources used. This uniformity not only reduces development complexity, but also provides developers with a high degree of scalability, enabling MCP to adapt to a variety of application scenarios.

     3. Server side: provider of resources and capabilities

     MCP Server is the resource provider in the system, responsible for connecting external capabilities and data required by AI models. These lightweight programs expose services through standard protocols, and their specific functions cover the following aspects:

•      Capability exposure: Providing specific functions through standardized interfaces, such as supporting LLMs to perform complex tasks (such as generating reports or calling APIs).

•     Tool and Data Access: Provide AI models with access to tools (e.g., calculators, code interpreters) and data sources (e.g., file contents, real-time weather information).

•     Database connection: Connect to the internal enterprise database or cloud storage to extract structured data for model use.

•     Service Integration: Collaborate with external services (e.g. YouTube API, stock price interface) to provide diverse information inputs to the model.

•     File operations: Supports file read and write operations, such as reading documents from a local disk or saving generated content to a specified location.

•     Specialized tasks: perform tasks in specific fields, such as image processing, speech transcription and other specialized functions.

     The workflow of the MCP server is clear and efficient: it receives requests from clients, performs corresponding operations (such as querying a database or calling a tool), and then returns the results to the AI ​​model in a standard format. This design gives the server great flexibility, and developers can customize server functions according to their needs to meet a wide range of scenarios from simple data retrieval to complex workflow management.