Artificial intelligence (AI) assistants are getting smarter, but have you ever wondered why they can't just read your files, browse your emails, or access corporate databases to give you more tailored answers? The reason is that today’s AI models are often confined to their respective platforms and cannot easily connect to different data sources or tools. 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 securely and bidirectionally connect to the external information and services you need. Next, we will introduce the definition, functions, and design concept of MCP in an easy-to-understand way, and explain how it works through metaphors and examples. In addition, we will share the initial reactions of the academic and development communities to MCP, discuss the challenges and limitations of MCP, and look forward to the potential and role of MCP in future AI applications.

The origin and goal of MCP: Building a data bridge for AI

With the widespread use of AI assistants, all walks of life have invested a lot of resources to improve 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 files, and internal enterprise systems), we often need to create customized integration solutions for each AI platform and each tool.

Not only is it cumbersome to develop and difficult to maintain, 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 required, which is almost impossible to expand with demand. This fragmented approach seems to have taken us back to the era when computers were not yet standardized. Every time a new device was connected, a dedicated driver and interface had to be installed, which was extremely inconvenient .

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

Some people figuratively compare it to the "USB-C interface" of the AI ​​world: just as USB-C standardizes device connections, MCP will also 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 answers.

How does MCP work? Universal "translator" for tools and materials

In order to lower the technical threshold, MCP adopts an intuitive Client-Server architecture.

MCP can be thought of as a coordinating "translator": one end is the AI ​​application (Client), such as a chatbot, smart editor, or any software that requires AI assistance; the other end is the data or service (Server), such as the company's database, cloud drive, email service, or any external tool.

Developers can write an MCP server (a lightweight program) for a certain data source, allowing it to provide the data or functions to the outside world in a standard format; at the same time, the MCP client built into the AI ​​application can communicate with the server according to the protocol.

The beauty of this design is that the AI ​​model itself does not need to directly call various APIs or databases. It only needs to send a request through the MCP client, and the MCP server will act as an intermediary to translate the AI's "intention" into specific operations of the corresponding service, and then transmit the results back to the AI ​​after execution. The whole process is very natural for users. They only need to give instructions to the AI ​​assistant in everyday language, and the rest of the communication details are handled by MCP behind the scenes.

Let’s take a concrete example : suppose you want an AI assistant to help you with your Gmail emails. First, you can install a Gmail MCP server and give it access to your Gmail account through the standard OAuth authorization process.

Later, when you talk to the AI ​​assistant, you can ask: "Please help me check which unread emails from my boss regarding quarterly reports?" When the AI ​​model receives this sentence, it will recognize that this is an email query task and use the MCP protocol to send a search request to the Gmail server. The MCP server uses the previously stored authorization credentials to access the Gmail API on your behalf to search for emails and return the results to the AI. The AI ​​then sorts the information and uses natural language to answer the email summaries you found. Similarly, if you then say "Please delete all marketing emails from last week", AI will send instructions to the server through MCP to execute the email deletion operation.

During the entire process, you don’t need to open Gmail directly . You can complete the tasks of checking and deleting emails just by communicating with AI. This is the powerful experience that MCP brings: the AI ​​assistant is directly connected to the operations of daily applications through a "context bridge".

It is worth mentioning that MCP supports two-way interaction. Not only can AI "read" external data, but it can also perform external actions through tools (such as adding calendar events, sending emails, etc.). It’s like AI not only gets a “book” of information, but also comes with a set of usable “toolboxes”. Through MCP, AI can autonomously decide to use a tool to complete a task at the appropriate time, such as automatically calling a database query tool to obtain data when answering a program question. This flexible context maintenance allows AI to remember relevant context when switching between different tools and data sets, 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 modularity, which enables the interaction between AI and the outside world to take a step further. Here are some important features of MCP:

• Open Standard : MCP is a protocol specification released as open source. Anyone can view the specification details and implement it. This openness means that it is not proprietary to any single manufacturer, reducing the risk of being tied to a specific platform. Developers can safely invest resources in MCP because once adopted, even if they switch AI service providers or models in the future, the new models introduced can still use the same MCP interface. In other words, MCP enhances the compatibility between models from different brands, avoids manufacturer lock-in, and brings more flexibility.

• Develop once, apply to many : In the past, developers could not directly apply the plug-in or integration they built for one AI model to another model; but with MCP, the same data connector can be reused by multiple AI tools. For example, you don’t have to write a separate integration for OpenAI’s ChatGPT and Anthropic’s Claude to connect to Google Drive. You only need to provide a “Google Drive server” that follows the MCP standard, and both can access 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 new models can directly use existing rich tools when they go online.

• Both context and tools are important: MCP is called "Model Context Protocol", which actually covers a variety of forms of providing AI-assisted information. According to the specification, the MCP server can provide three types of "primitives" for AI to use: the first is "Prompt", which can be understood as a pre-set instruction or template to guide or restrict the behavior of AI; the second is "Resource", which refers to structured data, such as file content, data tables, etc., which can be directly used as the context of AI input; the last is "Tool", which is an executable function or action, such as the aforementioned operations of querying the database and sending emails. Similarly, two primitives are defined on the AI ​​user side: "Root" and "Sampling". Root provides the server with access to the client 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 ​​to achieve advanced "model self-loop" behavior. Although ordinary users do not need to understand these technical details in depth, this design demonstrates the modular concept of MCP: splitting the elements required for AI to interact with the outside world into different types to facilitate future expansion and optimization. For example, the Anthropic team found that subdividing the traditional concept of "tool use" into types such as Prompt and Resource can help AI clearly distinguish different intentions and use contextual information more effectively.

• Security and authorization considerations : The MCP architecture fully considers data security and permission control . All MCP servers usually require user authorization before accessing sensitive data (for example, the aforementioned Gmail example obtains the token through OAuth) before they can operate. In the new version of the MCP specification, a standard authentication process based on OAuth 2.1 is introduced as part of the protocol to ensure that the communication between the client and the server is properly authenticated and authorized. In addition, for certain high-risk operations, MCP recommends retaining a human-in-the-loop review mechanism —that is, giving users the opportunity to confirm or reject when AI attempts to perform critical actions. These design concepts show that the MCP team attaches great importance to security and hopes to avoid introducing too many new risk points while expanding AI functions.

Initial reactions from academia and the development community

After MCP came out, it immediately sparked heated discussions in the technology circle and development community. The industry generally expresses expectations and support for this open standard .

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

In fact, OpenAI has integrated MCP into its Agents Development Kit and plans to provide support for it in the ChatGPT desktop application and response API soon. Such a statement is seen as an important milestone in the MCP ecosystem.

Not only leading companies are paying attention to MCP, but the developer community is also responding enthusiastically to it. On the technical forum Hacker News, the relevant discussion thread attracted hundreds of messages in a short period of time. Many developers view MCP as "finally a standardized LLM tool plug-in interface", believing that it does not bring any new features, but through a unified interface it is expected to significantly reduce the work of reinventing the wheel. One netizen summarized it vividly: "In short, MCP is trying to use the old tool/function calling mechanism to plug in a standardized universal plug-in interface for LLM. It does not introduce new capabilities, but hopes to solve the N×M integration problem and allow more tools to be developed and used." This view points out the core value of MCP: it lies in standardization rather than functional innovation, but standardization itself has a huge driving force for the ecosystem.

At the same time, some developers also raised questions and suggestions in the early stages. For example, some people have complained that the official document does not clearly define the term "context" and hope to see more practical examples to understand what MCP can do. Anthropic engineers also actively responded to the discussion, explaining: "The point of MCP is to bring the things you care about to any LLM application that has the MCP client installed. You can provide the database structure as a resource to the model (making it available for reference in the conversation at any time), or you can provide a tool to query the database. In this way, the model can decide for itself when to use the tool to answer questions." Through this explanation, many developers have a better understanding of the practicality of MCP. In general, the community is cautiously optimistic about MCP, believing that it has the potential to become a common industry standard, although it still takes time to observe its 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 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 that they are working with MCP to enhance the AI ​​capabilities of their own platforms.

Block's CTO even publicly praised: "Open technology like MCP is like a bridge from AI to real-world applications, making innovation more open, transparent and rooted in collaboration." This shows that the industry, from startups to large enterprises, has shown great interest in MCP, and cross-field cooperation has gradually become a trend. Anthropic Product Manager Mike Krieger also welcomed OpenAI's joining in a social post, revealing that "MCP is a thriving open standard with thousands of integrations underway and an ecosystem that continues to grow." These positive feedbacks show that MCP has achieved considerable recognition in its early stages.

Four challenges and limitations that MCPs may face

Although MCP has a promising future, there are still some challenges and limitations to overcome in its promotion and application:

• Popularity and compatibility across models : To maximize the value of MCP, more AI models and applications must support this standard. Currently, the Anthropic Claude series and some OpenAI products have expressed support, and Microsoft has also announced the launch of relevant integration for MCP (for example, providing MCP servers that allow AI to use browsers). However, it remains to be seen whether other major players such as Google, Meta, and various open source models will fully follow suit. If there are divergences in standards in the future (for example, different companies push different protocols), the original intention of open standards will be difficult to fully realize. Therefore, the popularization of MCP requires the industry to reach a consensus, and may even require the intervention of standard organizations to coordinate to ensure true compatibility and interoperability between different models.

• Difficulty of implementation and deployment : For developers, although MCP saves them the trouble of writing multiple sets of integration programs, the initial implementation still requires learning and development time. Writing an MCP server involves understanding JSON-RPC communication, primitive concepts, and interfacing with target services. Some small and medium-sized teams may not have the resources to develop on their own for the time being. However, the good news is that Anthropic has provided SDKs and sample code such as Python and TypeScript to help developers get started quickly. The community is also continuously releasing pre-built MCP connectors, covering common tools such as Google Drive, Slack, GitHub, etc. There are even cloud services (such as Cloudflare) that offer one-click deployment of MCP servers, simplifying the process of setting up MCP on remote servers. Therefore, as the tool chain matures, the implementation threshold of MCP is expected to gradually decrease. However, during the current transition period, enterprises still need to weigh factors such as development costs and system compatibility when introducing MCP.

• Security and permission control : Allowing AI models to freely access external data and operating tools inherently brings with it 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 bad people, the attacker may set up his own MCP server to impersonate the user and gain access to all the user's data, such as reading all emails, sending messages on behalf of others, and stealing sensitive information in bulk. Since this attack exploits legitimate API channels, it may even bypass traditional remote login warnings and go undetected. The second is the protection of the MCP server itself: as an intermediary that aggregates multiple service keys, once the MCP server is hacked, the attacker can gain access to all connected services, with disastrous consequences. This has been described as "stealing the keys to the kingdom with one click," especially in an enterprise environment where a single point of failure could allow attackers to gain access to multiple internal systems. Furthermore, there is a new threat of prompt word injection attacks: attackers may hide special instructions in files or messages to trick AI into inadvertently performing malicious operations. For example, a seemingly ordinary email may contain hidden instructions. When the AI ​​assistant reads the content of the email, the implanted hidden commands are triggered, causing the AI ​​to perform unauthorized actions (such as secretly transmitting confidential documents) through MCP. Since users often find it difficult to detect the existence of such obscure instructions, the traditional security boundary between "reading content" and "executing actions" becomes blurred, creating potential risks. Finally, too broad a scope of permissions is also a concern: in order to allow AI to flexibly complete a variety of tasks, MCP servers usually request broader authorizations (for example, full read, write, and delete permissions for emails, rather than just query permissions). In addition, MCP centrally manages access to many services. In the event of a data leak, attackers can cross-analyze data from multiple sources to obtain more comprehensive user privacy, or even legitimate MCP operators may abuse data across services to build a complete user profile. In summary, while MCP brings convenience, it also reshapes the original security model, requiring both developers and users to increase their risk awareness. In the process of promoting MCP, how to formulate comprehensive security best practices (such as more detailed permission control, enhanced credential protection, AI behavior supervision mechanism, etc.) will be an important issue.

• Specification Evolution and Governance : As an emerging standard, the specification details of MCP may be adjusted and upgraded based on feedback from actual 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, etc. to enhance security and compatibility. In the future, as more participants join, new functional modules may be added. How to coordinate the evolution of standards in an open community is also a challenge: there needs to be a clear governance mechanism to determine the direction of the standards, maintaining backward compatibility while meeting new requirements. In addition, when adopting MCP, enterprises must also pay attention to version consistency and 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 MCP matures, we have the opportunity to see a dedicated working group or standards organization lead its long-term maintenance to ensure that this open standard always serves the common interests of the entire AI ecosystem.

Future potential and application prospects of MCP

Looking ahead, Model Context Protocol (MCP) may play a key fundamental role in artificial intelligence applications, bringing about multiple impacts:

• Multi-model collaboration and modular AI : As MCP becomes more popular, we may see smoother collaboration between different AI models. Through MCP, an AI assistant can easily use the services provided by another AI system. For example, a text dialogue model can call the capabilities of an image recognition model through MCP (just encapsulate the latter into an MCP tool), thus achieving complementary advantages across models. Future AI applications may no longer be supported by a single model, but rather multiple AI agents with different specialties working together through standardized protocols. This is somewhat similar to the microservice architecture in software engineering: each service (model) performs its own duties, communicates and collaborates through standard interfaces to form a more powerful whole.

• Prosperous tool ecosystem : MCP has established a common "slot" for AI tools, which is expected to give birth to a prosperous third-party tool ecosystem. The developer community has begun to contribute various MCP connectors. As long as a new digital service emerges, someone may soon develop a corresponding MCP module. In the future, if users want their AI assistant to support a new feature, they may only need to download or enable a ready-made MCP plug-in without having to wait for official development support from the AI ​​vendor. This ecosystem model is a bit like the App Store for smartphones, except that "app" here is a tool or data source provided to AI for use. For enterprises, they can also establish their own internal MCP tool library for AI applications in various departments to share and gradually form an organizational-level AI ecosystem. In the long run, with the investment of a large number of developers, the richness of the MCP ecosystem will greatly enhance the application boundaries of AI assistants, allowing AI to truly integrate into more diverse business scenarios and daily life.

• New forms of standardization collaboration : Historical experience tells us that unified standards often lead to explosive innovations, just as the Internet is able to connect everything because of protocols such as TCP/IP and HTTP. As one of the key protocols in the AI ​​era, MCP has the potential to promote industry collaboration and communication on AI tool interfaces. It is worth noting that Anthropic adopts an open source collaborative approach to promote MCP and encourages developers to jointly improve the protocol. In the future, we may see more companies and research institutions participate in the formulation of MCP standards to make them more perfect. At the same time, standardization also lowers the threshold for entrepreneurial teams to enter the AI ​​tool market: startups can focus on creating creative tools because through MCP, their products can naturally be called by various AI assistants without having to adapt to multiple platforms separately. This will further accelerate the flourishing of AI tools and form a virtuous circle.

• A leap in AI assistant capabilities : In general, MCP will bring about 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 more deeply understand the user's current situation and the data at hand, so as to provide more appropriate assistance. For example, a business analysis assistant can connect to the financial system, calendar and email at the same time, and proactively remind you of important changes based on comprehensive information; or, in addition to reading the code base, the developer's programming AI can also access project management tools and discussion thread records, truly becoming an intelligent partner that understands the entire development context. Multi-mode and multi-functional AI assistants will no longer just chat and answer questions, but will be able to perform complex tasks and connect various services, becoming an even more indispensable helper in our work and life.

In summary, Model Context Protocol (MCP), as an emerging open standard, is building a bridge between AI models and the outside world. It shows us a trend: AI assistants will move from isolated islands to an interconnected and collaborative ecosystem. Of course, the implementation of new technologies is never achieved overnight. MCP still needs time to verify its stability and security, and all parties need to work together to develop best practices. However, what is certain is that standardization and collaboration are one of the inevitable directions of AI development. In the near future, when we use AI assistants to complete various complex tasks, we may rarely notice the existence of MCP - just as we no longer need to understand how HTTP works when surfing the Internet today. But it is precisely such agreements hidden behind the scenes that shape and support the prosperity of the entire ecosystem. The concept represented by MCP will promote the closer integration of AI into human digital life and open a new chapter for the application of artificial intelligence.