At the end of 2024, Anthropic open-source the Model Context Protocol (MCP), which quickly became one of the most highly regarded technical standards in the field of AI agents. If the big language model is the "brain" of the agent, then MCP is the "nervous system" that connects the brain with the external world - it defines how AI applications standardize the calling of tools, reading of data, and access of resources. This article provides a deep analysis of the MCP protocol from four dimensions: technical architecture, core primitives, ecological status, and practical implementation.
##Why MCP is needed: the "fragmentation dilemma" of tool calls
Before the emergence of MCP, the integration of AI applications with external systems was highly fragmented. Each Agent framework has its own tool calling specification: OpenAI uses the Function Calling interface, LangChain defines its own Tool abstraction, and various open-source frameworks have their own sets. Developers need to write specialized adaptation code for each data source or tool they access; Switching to a different Agent framework would render almost all of these adaptation codes obsolete.
This fragmentation brings triple costs: first, the cost of repetitive development, where the adaptation logic of the same tool needs to be written in different frameworks; The second is maintenance cost, as all adaptation layers need to be updated synchronously when upgrading tool interfaces; The third is ecological fragmentation, where tool providers cannot access and serve all agents at once. The core concept of MCP is to establish a unified standard protocol between AI applications and tools, similar to how USB interfaces unify peripheral connections.
##MCP architecture: client server model
MCP adopts the classic Client Server architecture, which includes three core roles:
Host is a process that runs AI applications, such as a chat client or IDE plugin that integrates MCP; The client establishes a connection with the server within the host and is responsible for protocol interaction; A server is a provider of tools, data resources, and prompts, which can be a local process or a remote service.
At the communication level, MCP is based on the JSON-RPC 2.0 messaging protocol. In terms of transmission method, standard input/output (STDIO) is usually used in local scenarios, and the server and client communicate through inter process pipelines; Remote scenarios support HTTP+SSE (Server Send Events) and other methods. This dual track design of "local STDio, remote HTTP" not only ensures low latency and security of local tools, but also leaves a channel for cloud tool serviceization.
##Three core primitives: Tools, Resources, and Prompts
The MCP protocol defines three core primitives, corresponding to three basic requirements for Agent interaction with the external world:
Tools are executable functions for models to call, such as querying weather, creating calendar events, and executing database queries. Tools are "actively called by the model" - the model decides when to call which tool based on user intent. The server declares the parameter structure of each tool through JSON Schema, and the client provides tool definitions to the model based on this.
Resources are read-only data assets, such as file contents, database records, and API return results. Resources are actively obtained by the application - the host or user can attach resources to the dialogue context as reference materials for model inference. Resources are located through URI, which is consistent with the URL design of web pages.
Prompts are reusable prompt word engineering products. The server can predefine a series of prompt word templates, which users or hosts can call upon to encapsulate complex task instructions into standardized processes. For example, a 'weekly report generation' prompt can be encapsulated as a complete instruction template that includes data source selection, output format, and style requirements.
Three types of primitives cover the main modes of Agent interaction: calling tools to change the world, reading resources to acquire knowledge, and applying templates to reuse experience.
##Official SDK and Ecological Development
Anthropic has officially provided two sets of SDKs for Python and TypeScript, significantly reducing the development threshold for MCP servers. A simple MCP server can be used by any client that supports MCP by implementing interfaces such as initialization handshake, tool list declaration, and tool call distribution as specified in the protocol.
At the ecological level, the adoption rate of MCP has exceeded many people's expectations. Within a few months after its release, several mainstream AI vendors and open source projects announced their support: OpenAI announced in 2025 that its Agent SDK is compatible with MCP; Google、 Major companies such as Microsoft have also integrated MCP support into their Agent development frameworks; Various databases, SaaS tools, and development platforms have launched official MCP servers one after another. In the community, the number of MCP servers spontaneously built by developers has rapidly increased, covering a wide range of scenarios from code hosting, cloud services to personal knowledge bases.
The ecological effect of "one development, everywhere operation" is beginning to emerge: tool providers can develop an MCP server that can be reused by all MCP supported Agent clients, which is similar to the explosive logic of the peripheral ecosystem after USB standardization in the past.
##Relationship with Function Calling: Evolution rather than Replacement
MCP is often compared to Function Calling, as they are not in a competitive relationship but rather design at different levels of abstraction. Function Calling is a model level capability - the model generates structured function call parameters based on the dialogue content; MCP is a system level integration standard that defines how tools are discovered, connected, and called. In practical implementation, the internal tool calls of MCP servers often need to be executed through the native function calling capability of the model. Function Calling can be understood as "the output format of the model", while MCP is "the interface of the tool"
## 企业落地：价值与注意事项
对企业而言，MCP的价值主要体现在三个方面：一是降低集成成本，存量系统可以通过开发MCP服务器快速接入Agent应用，无需改造既有接口；二是提升复用性，一套MCP服务器可以同时服务于内部多个Agent场景；三是安全边界可控，本地stdio模式下的MCP服务器运行在受控环境，权限与数据范围可以精细配置。
落地时需要注意的风险包括：工具权限管理——MCP服务器暴露的工具应有最小权限原则，避免Agent获得过大的操作范围；输入校验——模型的工具调用参数应经过严格校验，防止提示注入攻击；以及审计追踪——工具调用应有完整日志，满足合规与回溯需求。
## 结语
MCP正在从一项技术提案演变为AI Agent生态的事实标准之一。它的成功，本质上是"标准化"思想在AI应用层的胜利。对于开发者而言，掌握MCP协议的设计理念与实现方式，意味着拿到了连接AI与真实世界的"通用钥匙"。随着更多工具、数据源与服务平台加入MCP生态，AI Agent从"演示级"走向"生产级"的进程，正在被切实地加速。
【参考来源】Anthropic官方Model Context Protocol文档、MCP开源项目公开资料。