The emergence of the Model Context Protocol (MCP) heralds a fundamental transformation in the ecosystem of artificial intelligence applications. Launched by Anthropic in November 2024, MCP aims to standardize how AI applications interact with the world beyond their training data. Much like HTTP and REST laid the foundation for connections between web applications and services, MCP is establishing a unified standard for connecting AI models with various tools. While technical interpretations of MCP are common, its true power as a "standard" is often overlooked. The significance of standardization extends far beyond mere technical unification; it can drive a flywheel effect that fosters growth. Early adopters will benefit from this momentum, while those who ignore it may fall behind. ![MCP (1)](https://pic.chinaz.com/picmap/202504211736370401_0.jpg) *(Image Source: AI-generated)* ### From Chaos to Context: How MCP Empowers AI Applications Imagine a product manager like Lily, who needs to juggle multiple tools such as Jira, Figma, GitHub, Slack, Gmail, and Confluence to push projects forward. It wasn’t until the advent of large language models (LLMs) that she saw hope in their exceptional ability to integrate information. If all her team's tools could be connected to a single model, she could automate updates, draft communications, and answer questions on demand. However, each model had its unique way of connecting to services, which meant integrating new tools (like Gong’s call recordings) required building custom connections. This increased the difficulty of switching to better LLMs in the future. MCP changed this scenario. This open protocol standardizes the flow of contextual information to LLMs and has quickly gained support from industry giants like OpenAI, AWS, Azure, Microsoft Copilot Studio, and Google. Official SDKs cover mainstream programming languages such as Python, TypeScript, Java, C#, Rust, Kotlin, and Swift, while the community actively contributes SDKs for other languages like Go. The rapid adoption of MCP is remarkable. Now, Lily can run various functions through Claude and connect her work applications to a local MCP server. Status reports automatically generate drafts, and leader updates can be completed with a simple prompt. As new models emerge, she can easily replace the underlying model without rebuilding any integrations. Even when writing code, she can use OpenAI's model in Cursor and share the same MCP server with Claude, enabling the IDE to understand what she is building. MCP makes all of this seamless. ### The Power and Impact of Standards: Reshaping the AI Ecosystem Lily’s story reveals a simple truth: users are tired of fragmented tools and being locked into specific suppliers. Enterprises also don’t want to rewrite integrations every time they switch models. People crave the freedom to choose and use the best tools, and MCP is the key to achieving this vision. The impact of standardization is profound: - SaaS providers lacking strong public APIs face the risk of被淘汰. MCP tools rely on these APIs, and customers will demand support for their AI applications. As a fact-based standard forms, the lack of an open API will no longer be acceptable. - The AI application development cycle will significantly shorten. Developers no longer need to write custom integration code for simple AI applications but can directly integrate MCP servers with ready-made MCP clients (such as Claude Desktop, Cursor, and Windsurf). - Conversion costs will decrease significantly. Since integrations are decoupled from specific models, enterprises can freely switch between different models like Claude, OpenAI, and Gemini or even mix them without rebuilding underlying infrastructure. Future LLM providers will benefit from the existing ecosystem built around MCP, allowing them to focus on improving the cost-effectiveness of the models themselves. ### Addressing Challenges: Friction in the Standardization Process Like any standard, the promotion of MCP brings new challenges: - Trust is crucial. Currently, numerous MCP registries and community-maintained servers are emerging. However, if the operators of these servers cannot be trusted or controlled, sensitive information may be leaked to unknown third parties. SaaS companies should provide official servers, and developers should prioritize using official servers. - Quality varies. APIs are constantly evolving, and poorly maintained MCP servers may quickly become out of sync with the latest APIs. LLMs rely on high-quality metadata to select appropriate tools, and the lack of an authoritative MCP registry highlights the importance of official servers from trusted institutions. SaaS companies should maintain their servers as APIs evolve, and developers should seek official servers. - Large MCP servers increase costs and reduce usability. Bundling too many tools into a single server increases token consumption and confuses the model by presenting too many options. Building and distributing small, task-specific servers is critical. - Authorization and authentication challenges remain. These issues existed before MCP and have not been fully resolved. For example, when granting LLMs the ability to send emails, their behavior must be carefully controlled to avoid exceeding expected actions. Human supervision remains essential for high-risk operations. ### Looking Ahead: MCP Leading the Transformation of AI Application Infrastructure MCP is not a fleeting trend but a fundamental shift in AI application infrastructure. Much like widely adopted standards in the past, MCP is building a self-reinforcing flywheel: every new server, every new integration, and every new application strengthens its influence. New tools, platforms, and registries are emerging to simplify the process of building, testing, deploying, and discovering MCP servers. As the ecosystem continues to develop, AI applications will offer simpler interfaces to access new features. Teams adopting the MCP protocol will be able to deliver products faster and provide more comprehensive integration solutions. Companies providing public APIs and official MCP servers will be able to actively participate in the integration ecosystem, while those who move slowly may gradually lose competitiveness. As Noah Schwartz, the product lead at Postman, said, standardization is becoming a hidden yet powerful force driving the next wave of AI innovation.