MCP: Building an open and interoperable “super interface” for AI Agents

Recently, there are more and more articles about MCP. I also did a systematic study and wrote some code. This article mainly introduces the core concepts, and there will be some practical sharing on the right.

With the rapid development of artificial intelligence (AI) technology, various large language models (LLMs) continue to emerge. How to make these models more convenient and efficient to connect with the outside world has become a key proposition for building powerful AI agents. The Model Context Protocol (MCP)  was born in this context - it is committed to providing a universal and standardized interaction method for AI agents and the outside world (databases, APIs, file systems, etc.), thereby accelerating the implementation of AI applications and the prosperity of the ecosystem.

1. The core concept of MCP protocol

1. Standardized connections

In the traditional AI Agent construction process, developers often need to write various custom connection codes and integration logic for different data sources or services, which is cumbersome and error-prone. The MCP protocol aims to make the interaction between AI Agent and external tools or data sources more standardized and reusable by providing a unified specification, thereby greatly simplifying the development work and reducing unnecessary reinvention.

2. Common Interface

The MCP protocol can be understood as the "USB interface" of the AI ​​system: whether you want to connect to a database, a third-party API, or a local file, you can do so through this "universal interface." For developers, this greatly reduces the burden of writing various targeted integrations; for service providers, they only need to publish service capabilities once in accordance with the MCP protocol, so that more AI Agents can use them directly.

3. Resource Exposure

The MCP protocol focuses on " resource exposure ". When a service provider releases a function (such as email sending, calendar management, map query, etc.), it is equivalent to "exposing" this resource and its capabilities that can be used by AI Agents through the MCP protocol. AI Agents can read the corresponding metadata and incorporate it into their own context to better understand how to call these external capabilities.

4. Accelerate development and ecosystem construction

Through the standardized MCP protocol, various services and tools can be more easily connected to AI Agent. For developers, this means that they can not only focus on building the core capabilities of AI Agent, but also easily obtain and call other services to quickly implement complex functions. For the entire industry ecosystem, it provides a solid foundation for building an open and collaborative AI Agent ecosystem.

5. Improving AI system capabilities

Using the functional description provided by the MCP protocol, AI Agent can not only better understand external data and tools, but also automatically perform various tasks - from retrieving real-time data to calling automation tools. It enables AI Agent to have higher autonomy and efficiency, and is one step closer to true "autonomous intelligence".

2. Differences between MCP and Function Calling

In the current AI field, the concept of " Function Calling " has also attracted widespread attention: some specific models or platforms allow a set of functions to be pre-defined for LLM to automatically call based on the context. However, there are obvious differences between Function Calling and MCP protocols in essence:

Simply put, Function Calling is more like a "private function" extension of a certain LLM or platform; while MCP is a "public standard" that can span different models and platforms. Its core appeal is to build an open AI Agent ecosystem so that various services and AI Agents can be freely connected under the same set of protocols.

3. The Position of MCP in AI Agent Construction

The MCP protocol plays the role of " central nervous system " or " bridge " in the construction process of AI Agent - it connects AI Agent with the outside world, helping AI Agent to smoothly obtain context, call services, and automatically perform tasks.

1. Contextual information acquisition
     

• Based on the MCP protocol, AI Agent can easily retrieve necessary information from various data sources such as databases, APIs, local files, etc., providing more comprehensive contextual support for subsequent thinking, analysis, and decision-making.
• Tools and service calls
    
• Whether calling the calendar service to schedule a date, calling the map service to find a route, or calling the mail service to send an email, it can be implemented in a standardized way through the MCP protocol. This not only reduces the system's dependence on external tools, but also improves overall reliability and maintainability.
• Task Automation
    
• By obtaining more context, AI Agents can perform automated tasks in various platforms or services, such as automatically replying to emails, booking flights, or generating reports. This improves the autonomy of AI Agents and frees users from many tedious and repetitive tasks.
• Modularity and scalability
    
• Since the MCP protocol has a set of clear standards and interfaces, developers do not need to make major changes to the underlying structure when expanding the AI ​​Agent's functions. They only need to make the new service comply with the MCP specification. This modular design makes the system easier to expand, upgrade, and maintain.

4. In addition to MCP, what other key issues should be paid attention to?

Although the MCP protocol plays an important role in "connection and integration", building a truly powerful AI Agent also requires efforts in the following areas:

1. reasoning ability
     
• How can AI Agents better understand complex propositions? How can they perform common sense reasoning and causal inference? The MCP protocol alone is not enough to enable AI Agents to have stronger thinking abilities. The evolution of the underlying models and algorithms is the key.
• Planning capabilities
    
• Faced with tedious tasks, AI Agents need to develop action plans with clear steps and goals, and even make dynamic adjustments during execution. How to achieve this depends on in-depth research on planning algorithms or reinforcement learning technologies.
• Memory
    
• How can AI agents continuously accumulate experience in long-term interactions? How can they efficiently manage and retrieve key information in memory? The design of these memory mechanisms is crucial for the agent's behavioral continuity and self-learning.
• Security and Privacy
    
• Will AI Agent be used maliciously? When calling external tools, how to protect data from being intercepted or abused? A complete set of security strategies is required in the underlying architecture and usage scenarios.
• Explainability
    
• For many key application scenarios (finance, healthcare, etc.), whether the decision-making process of the AI ​​Agent is transparent and traceable directly affects the user's trust and also affects subsequent debugging and optimization.
• Ethical and social impact
    
• The popularization of AI technology has brought about many social controversies, such as job replacement, bias, privacy, and responsibility attribution, which all need to be considered and addressed from multiple aspects such as law, policy, and ethics.
• User Interaction
    
• No matter how powerful the AI ​​Agent is, it will be difficult to realize its true value if users cannot use it easily and naturally. Therefore, how to design a simple and friendly interactive interface that is close to user needs is also a major challenge in building an AI Agent.

5. Problems that MCP cannot solve

Although the MCP protocol provides an ideal standardized framework for connecting AI Agents to the outside world, it is not a panacea and still seems to be inadequate in the following areas:

1. Agent’s core intelligence capabilities
     
• MCP can help "connect to external services", but it cannot automatically enable AI Agents to have stronger reasoning, planning, learning, and language understanding capabilities. These still require continuous investment in model architecture, algorithm research, and data training.
• Service quality and reliability
    
• If the service accessed through the MCP protocol is unstable or has low performance, MCP cannot optimize or remedy it. It is only responsible for transmission and connection.
• Data Security and Privacy
    
• Although security standards can be agreed upon in MCP, the MCP protocol cannot completely eliminate security risks if the service provider's own security management is not in place or there are loopholes in the network transmission itself.
• Ethical and social impact
    
• Social issues such as bias, privacy, and responsibility definition essentially require the joint efforts of multiple parties. As a technical standard, MCP cannot directly provide the "ultimate solution" to these complex issues.

Last words

The MCP protocol lays the foundation for building a more open and interoperable AI Agent ecosystem. By providing unified resource exposure and access specifications, it enables AI Agents to call external services more easily, greatly accelerating application implementation and function expansion.

However, MCP cannot cover all the challenges faced by AI agent construction. Stronger reasoning, planning and memory capabilities, safer and more explainable system design, as well as ethical and social impact issues also require continuous investment.

Only under the premise of multi-dimensional coordinated advancement can we truly usher in an era of "super AI Agent" that is feature-rich, reliable and responsible. In this process, the MCP protocol will play a vital role and become a "key link" on the road of AI Agent towards autonomous intelligence.