USB-C Interface in the AI World: What is the Model Context Protocol (MCP)? An Interpretation of the Universal Context Protocol for AI Assistants

Artificial intelligence (AI) assistants are becoming increasingly Satoshi, but have you ever wondered: why can't they directly read your documents, browse your emails, or visit corporate databases to provide more relevant answers? The reason is that today's AI models are often confined to their respective platforms, making it difficult to easily connect different data sources or tools. The Model Context Protocol (MCP) is a new open standard created to solve this problem.

In short, MCP is like a "universal interface" built for AI assistants, allowing various AI models to safely and bidirectionally connect to the external information and services you need. Next, we will introduce the definition, functionality, and design philosophy of MCP in an easy-to-understand manner, using metaphors and examples to illustrate how it works. Additionally, we will share the initial reactions from academia and the development community regarding MCP, discuss the challenges and limitations faced by MCP, and look forward to the potential and role of MCP in future artificial intelligence applications.

The origin and goal of MCP: to build a data bridge for AI.

With the widespread application of AI assistants, various sectors have invested significant resources to enhance model capabilities, but the gap between models and data has become a major bottleneck.

Currently, whenever we want AI to learn from new data sources (such as new databases, cloud documents, or internal enterprise systems), we often need to create customized integration solutions for each AI platform and each tool.

Not only is development cumbersome and difficult to maintain, but it also leads to the so-called "M×N integration problem": if there are M different models and N different tools, theoretically, M×N independent integrations are needed, making it nearly impossible to scale with demand. This fragmented approach seems to take us back to the era before computers were standardized, where each time a new device was connected, it was necessary to install dedicated drivers and interfaces, which is extremely inconvenient.

The purpose of MCP is to break down these barriers and provide universal and open standards to connect AI systems with various data sources. Anthropic launched MCP in November 2024, hoping to allow developers to no longer have to develop separate "plugs" for each data source, but instead to communicate all information using a standard protocol.

Some have vividly compared it to the "USB-C interface" of the AI world: just as USB-C standardizes device connections, MCP will provide AI models with a unified "language" to access external data and tools. Through this common interface, cutting-edge AI models will be able to break through the limitations of information silos, obtain the necessary contextual information, and generate more relevant and useful responses.

How does MCP work? The universal "translator" of tools and data.

To lower the technical barrier, MCP adopts an intuitive Client-Server architecture.

You can think of MCP as a "translator" that coordinates in the middle: on one end is the AI application (Client), such as chatbots, smart editors, or any software that requires AI assistance; on the other end are the data or services (Server), such as the company’s database, cloud storage, email services, or any external tools.

Developers can write an MCP server (a lightweight program) for a specific data source, allowing it to provide the data or functionality externally in a standard format; at the same time, the built-in MCP client in the AI application can communicate with the server according to the protocol.

The beauty of this design lies in the fact that the AI model itself does not need to directly call various APIs or databases; it only needs to send requests through the MCP client, and the MCP server will act as an intermediary, translating the AI's "intent" into specific operations corresponding to the services. After execution, it will return the results to the AI. The entire process is very natural for the user, who only needs to give instructions to the AI assistant in everyday language, while the rest of the communication details are handled by the MCP behind the scenes.

For example, suppose you want an AI assistant to help you manage your Gmail emails. First, you can install a Gmail MC server and allow that server to gain access to your Gmail account through the standard OAuth authorization process.

Later, when talking to the AI assistant, you can ask, "Help me check what unread emails my boss sent me about the quarterly report?" When the AI model receives this sentence, it recognizes that it is an email query task and uses the MCP protocol to make a search request to the Gmail server. The MCP server uses the previously stored authorization credentials to search for emails on your behalf in the Gmail API and returns the results to AI. The AI then collates the information and answers the summary of the emails you find in natural language. Similarly, if you go on and say, "Please delete all marketing emails from last week," the AI will send instructions to the server via MCP to delete the emails.

Throughout the entire process, you do not need to directly open Gmail; you can complete the tasks of checking and deleting emails just through a conversation with AI. This is precisely the powerful experience brought by MCP: the AI assistant directly connects to the operations of everyday applications through a 'context bridge'.

It is worth mentioning that MCP supports bidirectional interaction, allowing not only AI to "read" external data but also to execute actions externally through tools (such as adding calendar events, sending emails, etc.). This is akin to AI not only having access to the "book" of data but also being equipped with a usable "toolbox." Through MCP, AI can autonomously decide to use a specific tool to complete tasks at appropriate moments, such as automatically invoking a database query tool to retrieve information when answering programming questions. This flexible context maintenance enables AI to remember relevant background information while switching between different tools and datasets, improving the efficiency of solving complex tasks.

Four Major Features of MCP

The reason why MCP has attracted attention is that it integrates multiple design concepts such as openness, standardization, and modularization, further enhancing the interaction between AI and the external world. Here are several important features of MCP:

Open Standard: MCP is a protocol specification released in open source form. Anyone can view its specification details and implement it. This openness means it is not owned by any single vendor, reducing the risk of being tied to a specific platform. Developers can confidently invest resources into MCP, as once adopted, even if they switch AI service providers or models in the future, the new models can still use the same MCP interface. In other words, MCP enhances compatibility between models from different brands, avoiding vendor lock-in and providing more flexibility.

One development, multiple applications: In the past, plugins or integrations developed for a specific AI model could not be directly applied to another model; however, with MCP, the same data connectors can be reused by various AI tools. For example, you don't have to write a separate integration program for connecting Google Drive for OpenAI's ChatGPT and Anthropic's Claude; you only need to provide a "Google Drive server" that follows the MCP standard, and both can connect and use it. This not only saves development and maintenance costs but also makes the AI tool ecosystem more prosperous: the community can share various MCP integration modules, and when new models are launched, they can directly utilize the existing rich tools.

Context and Tools are equally important: MCP, named "Model Context Protocol", actually encompasses various forms of AI-assisted information. According to the specifications, the MCP server can provide three types of "primitive (" for AI use: the first is "Prompt", which can be understood as predefined instructions or templates used to guide or constrain the behavior of AI; the second is "Resource", referring to structured data such as document content, data tables, etc., which can directly serve as context for AI input; finally, there is "Tool", which are executable functions or actions, such as querying a database or sending emails. Similarly, the AI client also defines two types of primitives: "Root" and "Sampling". Root provides access for the server to visit the client’s file system (for example, allowing the server to read and write the user's local files), while Sampling allows the server to request an additional text generation from the AI, used to achieve advanced "model self-looping" behavior. Although ordinary users do not need to understand these technical details in depth, this design demonstrates the modular thinking of MCP: breaking down the elements required for AI to interact with the external world into different types, facilitating future expansion and optimization. For example, the Anthropic team found that breaking down the traditional concept of "tool usage" into types such as Prompt and Resource helps AI clearly distinguish different intents and utilize contextual information more effectively.

Security and authorization considerations: MCP's architecture fully considers data security and permission control. All MCP servers usually require user authorization (e.g., the Gmail example above to obtain a token through OAuth) when accessing sensitive data. In the new version of the MCP specification, a standard authentication process based on OAuth 2.1 has been introduced as part of the protocol to ensure that communication between clients and servers is properly authenticated and authorized. In addition, for certain high-stakes operations, MCP recommends preserving a human-in-loop moderation mechanism — that is, giving the user the opportunity to confirm or reject when the AI attempts to perform a critical action. These design concepts show that the MCP team attaches great importance to security and wants to expand the capabilities of AI while avoiding introducing too many new risk points.

Initial reactions from the academic community and the developer community

After the launch of MCP, it immediately sparked enthusiastic discussions in the tech community and among developers. The industry generally expresses anticipation and support for this open standard.

For example, OpenAI CEO Sam Altman announced in a March 2025 post that OpenAI will include support for the Anthropic MCP standard in its products. This means that the popular ChatGPT assistant will also be able to access various data sources through MCP in the future, indicating a trend of the two AI labs working together to promote common standards. "Everyone loves MCP and we're excited to add support to it across all of our products," he says.

In fact, OpenAI has integrated MC into its Agents development kit and plans to support it soon on the ChatGPT desktop application and response API. This statement is seen as an important milestone for the MC ecosystem.

Not only are leading companies paying attention, but the developer community is also responding enthusiastically to MCP. On the technical forum Hacker News, related discussion threads quickly attracted hundreds of comments. Many developers see MCP as "the standardized LLM tool plugin interface finally appearing," believing that it does not bring new functions but hopes to significantly reduce the redundant work of reinventing the wheel through a unified interface. One netizen vividly summarized: "In short, MCP attempts to use the old tool/function call mechanism to provide LLM with a standardized universal plugin interface. It does not introduce new capabilities but aims to solve the N×M integration problem, allowing more tools to be developed and used." This perspective highlights the core value of MCP: it lies in standardization rather than functional innovation, but standardization itself has a tremendous driving effect on the ecosystem.

At the same time, some developers raised questions and suggestions at the early stage. For example, some people have complained that the definition of the term "contextual )context(" in official documents is not clear enough, and it would be desirable to see more practical examples to understand what the MCP can do. Anthropic's engineers also responded positively during the discussion, explaining, "The gist of MCP is to bring what you care about to any LLM application with an MCP client. You can provide the database structure to the model as a resource (so that it can be accessed at any time in the conversation), or you can provide a tool to query the database. This allows the model to decide for itself when to use the tool to answer questions." Through this explanation, many developers have a better understanding of the usefulness of MCP. Overall, the community is cautiously optimistic about MCP, believing that it has the potential to become an industry common denominator, although it will take time to see the maturity and actual benefits.

It is worth mentioning that MCP attracted a group of early adopters shortly after its release. For example, payment company Block (formerly known as Square) and multimedia platform Apollo have integrated MCP into their internal systems; developer tool companies such as Zed, Replit, Codeium, and Sourcegraph have also announced collaborations with MCP to enhance the AI capabilities of their platforms.

The CTO of Block even publicly praised: "Open technologies like MCP serve as a bridge for AI to reach real-world applications, making innovation more open and transparent, and rooted in collaboration." This shows that the industry, from startups to large enterprises, has shown strong interest in MCP, and cross-disciplinary collaboration is gradually forming a trend. Mike Krieger, Chief Product Officer at Anthropic, also welcomed OpenAI's participation in a community post, revealing that "MCP, as a thriving open standard, has thousands of integrations underway, and the ecosystem continues to grow." These positive feedbacks indicate that MCP has achieved a considerable level of recognition since its initial launch.

Four Challenges and Limitations that MCP May Face

Although the prospects for MC are promising, there are still some challenges and limitations to overcome in promotion and application:

Cross-model popularization and compatibility: To maximize the value of MCP, more AI models and applications must support this standard. Currently, the Anthropic Claude series and some products from OpenAI have expressed support, and Microsoft has also announced related integrations for MCP (such as providing MCP servers that allow AI to use browsers). However, it remains to be seen whether other major players like Google, Meta, and various open-source models will fully follow suit. If discrepancies in standards arise in the future (for example, if different companies promote different protocols), the original intention of open standards will be difficult to fully realize. Therefore, the popularization of MCP requires consensus within the industry and may even need standard organizations to intervene and coordinate to ensure true compatibility and interoperability between different models.

Implementation and deployment difficulty: For developers, although MCP eliminates the hassle of writing multiple integration programs, initial implementation still requires investment in learning and development time. Writing an MCP server involves understanding JSON-RPC communication, primitive concepts, and interfacing with the target service. Some small to medium-sized teams may temporarily lack the resources to develop it themselves. However, the good news is that Anthropic has provided SDKs and sample code in Python, TypeScript, and other languages, making it easier for developers to get started quickly. The community is also continuously releasing pre-built MCP connectors covering common tools such as Google Drive, Slack, GitHub, and more. There are even cloud services (such as Cloudflare) offering one-click deployment solutions for MCP servers, simplifying the process of setting up MCP on remote servers. Therefore, as the toolchain matures, the implementation threshold for MCP is expected to gradually decrease. However, during the current transitional period, enterprises adopting MCP still need to weigh factors such as development costs and system compatibility.

Security and permission control: Giving AI models the freedom to call external data and operational tools comes with new security risks. The first is the security of access credentials: MCP servers usually need to save credentials for various services (such as OAuth tokens) to perform operations on behalf of users. If these credentials are stolen by unscrupulous people, the attacker may set up their own MCP server to impersonate the user, and then obtain access to all the user's data, such as reading all emails, sending messages, and stealing sensitive information in batches. Since this attack exploits a legitimate API channel, it may even bypass traditional remote login alerts without detection. The second is the protection of the MCP server itself: as an intermediary that aggregates multiple service keys, once the MCP server is compromised, the attacker can gain access to all connected services, with unimaginable consequences. This has been described as "stealing the keys to an entire kingdom with one click," especially in an enterprise environment where a single point of failure can allow attackers to drive straight into multiple internal systems. There is also a new threat of prompt injection attacks: attackers may trick AI into inadvertently performing malicious actions by hiding special instructions in files or messages. For example, a seemingly ordinary email contains a hidden command, and when the AI assistant reads the content of the email, the implanted hidden command is triggered, allowing the AI to perform unauthorized actions through MCP (such as secretly transmitting confidential documents). Since users are often unaware of the existence of such cryptic instructions, the traditional security boundary between "reading content" and "performing actions" is blurred here, creating potential risks. Finally, the wide range of permissions is also a concern: in order to make AI flexible to complete a variety of tasks, MCP servers often request broad authorization (such as read-write discretion over messages, rather than just queries). Coupled with the fact that MCP centrally manages visits to many services, in the event of a data breach, attackers can cross-analyze data from multiple sources for more comprehensive user privacy, or even legitimate MCP operators may abuse cross-service data to build a complete user profile. All in all, MCP brings convenience while reshaping the original security model, requiring both developers and users to be more aware of risks. In the process of promoting MCP, how to develop sound security best practices (such as more detailed permission control, strengthened credential protection, AI behavior supervision mechanism, etc.) will be an important issue.

Specification Evolution and Governance: As an emerging standard, MCP's specification details may be adjusted and upgraded as feedback from real-world applications. In fact, Anthropic released an updated version of the MCP specification in March 2025, introducing improvements such as the aforementioned OAuth standard authentication, instant two-way communication, batch requests, and more to enhance security and compatibility. In the future, new functional modules may be expanded as more participants join. How to coordinate the evolution of norms in the open community is also a challenge: there needs to be clear governance mechanisms to determine the direction of standards, maintain backward compatibility and meet new requirements. In addition, enterprises should also pay attention to version consistency when adopting MCP to ensure that the client and server follow the same version of the protocol, otherwise poor communication may occur. However, the evolution of such standardized protocols can refer to the development history of Internet standards and be gradually improved under community consensus. As MCPs mature, we have the opportunity to see dedicated working groups or standards organizations leading their long-term maintenance, ensuring that this open standard always serves the common good of the entire AI ecosystem.

The future potential and application outlook of MCP

Looking to the future, the Model Context Protocol (MCP) may play a key foundational role in artificial intelligence applications, bringing about multifaceted impacts:

Multi-Model Collaboration and Modular AI: With the popularity of MCP, we may see smoother collaboration between different AI models. Through MCP, one AI assistant can conveniently utilize the services provided by another AI system. For example, a text dialogue model can invoke the capabilities of an image recognition model via MCP (by simply wrapping the latter as an MCP tool), achieving complementary advantages across models. Future AI applications may no longer rely on a single model but rather on multiple AI agents with different specialties cooperating through standardized protocols. This is somewhat similar to the microservices architecture in software engineering: each service (model) performs its own function, communicating and collaborating through standardized interfaces to form a more powerful whole.

Prosperous Tool Ecosystem: MCP has established a common "slot" for AI tools, which is expected to foster a thriving third-party tool ecosystem. The developer community has already started contributing various MCP connectors, and as new digital services emerge, someone may quickly develop corresponding MCP modules. In the future, if users want their AI assistants to support a new feature, they might only need to download or enable a ready-made MCP plugin, without having to wait for official support from the AI provider. This ecological model is somewhat similar to the App Store for smartphones, except that the "apps" here are tools or data sources provided for AI use. For enterprises, they can also build their own internal MCP tool library for sharing among various departments' AI applications, gradually forming an organization-level AI ecosystem. In the long run, with a large number of developers participating, the richness of the MCP ecosystem will significantly enhance the application boundaries of AI assistants, allowing AI to truly integrate into more diverse business scenarios and daily life.

New forms of standardized collaboration: Historical experience tells us that unified standards often give rise to explosive innovation — just as the internet connected everything through protocols like TCP/IP and HTTP. As one of the key protocols of the AI era, MCP has the potential to facilitate collaboration across the industry in the integration of AI tools. It is worth noting that Anthropic is promoting MCP through an open-source collaborative approach, encouraging developers to improve the protocol together. In the future, we may see more companies and research institutions participating in the formulation of MCP standards, making it more refined. At the same time, standardization also lowers the barriers for startup teams to enter the AI tools market: startups can focus on creating innovative tools, because through MCP, their products can naturally be accessed by various AI assistants without the need to adapt to multiple platforms individually. This will further accelerate the flourishing of AI tools, creating a virtuous cycle.

The Leap in AI Assistant Capabilities: In summary, what MCP brings will be an upgrade in AI assistant capabilities. Through plug-and-play contextual protocols, future AI assistants will be able to access all digital resources that users already have, from personal devices to cloud services, from office software to development tools. This means that AI can understand the user's current context and the data at hand more deeply, thus providing more relevant assistance. For example, a business analytics assistant can simultaneously connect to financial systems, calendar schedules, and emails, proactively reminding you of important changes; or, a programming AI for developers can not only read code repositories but also connect to project management tools and discussion records, truly becoming an intelligent partner that understands the entire development context. Multi-modal and multi-functional AI assistants will no longer just answer questions in a chat, but will be able to execute complex tasks, link various services, and become an indispensable helper in our work and lives.

In summary, the Model Context Protocol (MCP) is an emerging open standard that is bridging the gap between AI models and the external world. It reveals a trend: AI assistants will transition from isolated systems to a collaborative networked ecosystem. Of course, the implementation of new technologies is never a simple task; MCP still requires time to validate its stability and security, and all parties must work together to establish best practices. However, it is certain that standardization and collaboration are one of the inevitable directions for AI development. In the near future, when we use AI assistants to complete various complex tasks, we may hardly notice the existence of MCP—just as we no longer need to understand how HTTP works when browsing the web today. Yet it is precisely this deeply embedded protocol that shapes and supports the prosperity of the entire ecosystem. The ideas represented by MCP will drive AI to integrate more closely into human digital life, opening a new chapter for artificial intelligence applications.

This article AI World’s USB-C Interface: What is the Model Context Protocol (MCP)? Interpretation of the universal context protocol for AI assistants first appeared in Chain News ABMedia.