In the fast-paced world of AI development, building applications based on Large Language Models (LLMs) has become an industry focus. However, before the advent of MCP (Model Context Protocol), developers faced a host of tricky challenges when building LLM applications. These issues not only slowed down development efficiency but also limited the practical implementation of large models. This article will delve into the core value of the MCP protocol, its architectural design, and the key differences between MCP and Function Calling, helping developers gain a better understanding of this essential tool for enhancing the scalability of LLM applications.
The 5 Core Pain Points of LLM Application Development Before MCP
For engineers working on LLM application development, “reinventing the wheel” and “functional limitations” are common hurdles in daily work. These problems stem from the lack of a standardized approach for interactions between models and external systems, and can be broken down into the following five key areas:
1. Interface Fragmentation: Repetitive Adaptation Code Development
LLM applications often need to connect to a variety of data sources (such as MySQL databases, MongoDB document stores) and external tools (like Cursor IDE, Slack collaboration tools). However, the calling interfaces, formats, and authentication methods of these different resources vary greatly. Developers have to write separate adaptation code for each external resource—for instance, handling OAuth 2.0 authentication when connecting to the GitHub API, and writing SQL query logic when linking to a local database. This repetitive development consumes significant time and human resources.
2. Functional Limitations: Static Knowledge Fails to Meet Real-Time Needs
The pre-trained knowledge of LLMs is static, meaning they cannot proactively access real-time information or call tools. For example, when a user asks for “2025 latest AI industry report data,” the model can only rely on outdated data from before its training cutoff. In programming scenarios, the model cannot directly call the debugging functions of an IDE, and can only provide code suggestions without verifying execution results. This “passive response” model leads to delayed answers and insufficient practicality, making it difficult to meet the complex needs of enterprise-level scenarios.
3. High Development and Maintenance Costs: Poor System Scalability
Every LLM application project requires designing the interaction logic between the model and external systems from scratch, with no reusable framework available. What’s more, if an external interface changes (such as adjusted API parameters or updated authentication methods), the entire interaction layer of the application must be redeveloped and debugged. For example, when an e-commerce LLM customer service system connects to a new order query API, developers need to modify a large amount of associated code, causing maintenance costs to grow exponentially with system complexity.
4. Ecosystem Fragmentation: Tool Silos Hinder Technical Sharing
LLM tool plugins developed by different teams are often incompatible with each other, creating “tool silos.” For instance, an enterprise knowledge base retrieval plugin developed by Company A cannot be directly integrated into the LLM customer service system of Company B; nor can a data analysis tool built by Team C connect with a code generation platform from Team D. This fragmentation prevents the reuse of high-quality tools, forcing developers to repeatedly build similar functions and hindering the collaborative development of the AI application ecosystem.
5. Security and Debugging Risks: Lack of Standardization Causes Hidden Dangers
Non-standardized communication protocols are prone to security vulnerabilities—for example, unauthenticated data calling paths may lead to the leakage of sensitive information. At the same time, fragmented interaction methods make error troubleshooting extremely difficult. When an LLM application encounters data return errors, developers have to check the call logs of each external interface one by one, which is time-consuming and labor-intensive, increasing the stability risks of the system after launch.
What is MCP: A Standardized Protocol Built for LLM Interactions
To address the aforementioned pain points in LLM application development, Anthropic launched MCP (Model Context Protocol)—an open, standardized protocol specifically designed to enable efficient communication between large language models and external data sources/tools. By unifying interaction formats and communication rules, MCP allows LLMs to connect to various external systems as easily as “calling a local function,” fundamentally improving development efficiency and system scalability.
MCP’s Architectural Design: A Flexible Client-Server Collaboration Model
MCP adopts a classic client-server architecture, supporting bidirectional communication and standardized JSON message formats. Its core advantage lies in “one-time adaptation, multi-end reuse.” Developers do not need to write separate interfaces for different external services; instead, they can achieve seamless integration between models and tools through the MCP protocol. The core components include the following five parts:
- MCP Host: Serving as the “entry carrier” of the protocol, it refers to applications that want to access external data via MCP—such as the Claude Desktop client, Cursor IDE, or enterprise self-developed LLM platforms.
- MCP Client: Acting as a “translator,” it maintains a one-to-one connection with the server. It converts user requests or model instructions into a standardized format, sends them to the MCP Server, and simultaneously receives results returned by the server and forwards them back to the model or user.
- MCP Server: Functioning as the “dispatch center” for external resources, it is a lightweight program that exposes specific functions. After receiving requests from the MCP Client, it calls the corresponding data sources or tools based on requirements, processes the data, and returns the results to the client.
- Local Data Sources: These are local resources that the MCP Server can securely access, such as the file system on a computer, local databases (SQLite, PostgreSQL), or services within a local area network.
- Remote Services: These are external systems accessible via the Internet, such as the GitHub API, Slack Webhook, and weather query interfaces. The MCP Server can connect to them through standardized protocols. Here’s a real-world application example: When a developer writes code using the Cursor IDE, they can use the MCP protocol to enable Cursor to call Slack’s API to read the user’s requirement messages, and then automatically trigger the Claude model to generate code snippets. The entire process requires no manual tool switching or adaptation code writing, achieving seamless connection between “requirements and development.”
Key Differences Between MCP and Function Calling
Many developers confuse MCP and Function Calling. While both are used to enhance the interaction capabilities between LLMs and external systems, their positioning and functions are fundamentally different. The table below provides a detailed comparison from dimensions such as definition and functionality:
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