MCP: Perfect collaboration between AI and tools, increasing efficiency by N times!

This article may be a bit boring, but believe me, if you can stick to the end, you will definitely have a deeper understanding of the concept of MCP.

1. What is MCP? What problem does it solve?

MCP (Model Context Protocol) is an open standard protocol designed for AI applications (especially large language models LLM) to interact with external tools and data sources. Its goal is to unify the way AI connects to the outside world, like a "USB-C port for AI", and solve the following core problems:

• LLMs are traditionally isolated from external data and cannot acquire new information in real time.
• Without unified standards, connecting M models to N tools requires M×N custom developments, which is extremely inefficient.
• Lack of standardization leads to fragile integrations that are difficult to maintain.
• It is necessary to provide a foundation for agentic AI so that it can autonomously plan and execute complex tasks.

2. What are the core design principles of MCP? Why is it designed this way?

The design of MCP follows the following principles:

• Standardization : Establish a unified interface to simplify the connection between AI and tools/data sources.
• Interoperability : Different vendors, models, and tools can work together seamlessly.
• Composability : Supports modularity and plug-and-play, allowing complex AI systems to be built like building blocks.
• Easy to develop : The server is simple to implement, and the host is responsible for complex orchestration, which lowers the development threshold.
• Security and isolation : The three-tier architecture of host-client-server ensures the security boundary of data and operations. The server runs locally by default and requires user authorization.
• Progressive features : The protocol core is simple, supports capability negotiation and flexible expansion, and is compatible with future developments.

3. What is the structure and working mechanism of MCP?

MCP adopts a three-tier architecture of "client-host-server":

• Host : Responsible for managing the life cycle, permissions, and security policies of clients and servers, and is the coordination and security core of the entire system.
• Client : maintains an isolated stateful session with a specific MCP server and is responsible for capability negotiation, message routing, etc.
• Server : exposes tools, resources or tips in a standard way, runs independently, and can be deployed locally or remotely.
• Communication protocol : Based on JSON-RPC 2.0, supporting stateful sessions and multiple transmission methods (such as Stdio, SSE-HTTP).
• Three primitives : tools (executable functions), resources (read-only data streams), and prompts (instruction templates), which are controlled by the model, application, and user respectively.

4. What are the main advantages brought by MCP?

• Standardization and interoperability : Unify the protocol, achieve plug-and-play like USB-C, and promote ecological prosperity.
• Simplify development : Reduce the complexity of M×N integration to M+N, make tools reusable, and let developers focus on high-level logic.
• Enhance LLM capabilities : acquire external data in real time, dynamically discover and call tools, and improve context awareness and intelligent agent capabilities.
• Security and privacy : clarify boundaries, user authorization, and server isolation to strengthen security and privacy protection.
• Scalable and composable : Modular design supports continuous expansion of the ecosystem and flexible assembly of complex systems.

5. What are the key shortcomings and challenges of MCP currently?

• The technology is not yet mature : the protocols and toolchains are in their early stages, the specifications may change, and the ecosystem is still small.
• Imperfect identity and security mechanisms : key links such as identity verification and management need to be completed.
• Risk of ecological fragmentation : If the industry does not have unified standards, or if the number of suppliers expands too much, multiple incompatible "styles" may appear, reducing interoperability.
• Performance and operation and maintenance overhead : The protocol introduces additional communication and state management, which may affect performance under high concurrency and large-scale deployment.
• Learning curve : New concepts and integration methods require developers to learn and adapt.

6. What are the key factors affecting the future development of MCP?

answer:

• Industry collaboration : Broad adoption and deep collaboration are prerequisites for the success of MCP.
• Standard governance : An open, transparent and inclusive governance mechanism needs to be established to prevent fragmentation.
• Improve security and identity mechanisms : prioritize filling gaps in identity verification and management.
• Ecosystem construction : Continuously enrich the tool and server ecosystem and lower the development threshold.
• Shift in mindset : Promote AI system design from "isolated islands" to a new paradigm of "composable, scalable, and securely connected".

7. Can MCP only be used in AI scenarios? Can it be used for ordinary system integration without AI?

• MCP can be used for ordinary system integration without AI, but this is not its original design.
• The core value and many features of MCP (such as dynamic tool discovery, contextual enhancement, and prompt primitives) are mainly for AI systems (especially LLMs and agents).
• Therefore, MCP is best suited to provide AI systems with a standardized way to interact with the outside world.

8. What is the essential difference between MCP and traditional API integration methods?

MCP emphasizes standardization, dynamic discovery and context enhancement, and supports plug-and-play and modular combination; traditional API integration is usually customized and statically configured, lacking unified standards and context awareness. MCP can greatly reduce integration complexity and improve system flexibility and scalability.

9. The relationship between MCP and tool use (or function calling) of large language models

There is a very close and complementary relationship between MCP and tool use or function calling of large language models. They can be understood as a two-stage process:

• Phase 1: Function Calling (Tool Use by LLM)

• This means that the LLM understands the user's intent based on the user's input (prompt) and the current conversation context, and decides that an external tool or function needs to be used to complete a specific task or obtain additional information.
• LLM generates a structured "function call" request that contains the name of the tool to be called and the parameters to be passed to the tool. For example, if the user asks "What's the weather like in San Francisco right now?", LLM might generate a function call like get_weather(location="San Francisco") function call.
• Different LLM providers (such as OpenAI, Anthropic, Google) have their own way of implementing function calls and output formats.

• Phase 2: MCP executes function call

• Once the LLM generates a function call instruction, a mechanism is needed to actually execute the call and obtain the result. MCP plays a key role here, providing a standardized framework to handle these function calls generated by LLM. The role of MCP includes:

• Tool Discovery : The MCP server announces its available tools to the host (usually an AI application). This allows the LLM to know which tools can be called
• Invocation : When the LLM decides to use a tool, the MCP host (or its client) sends a request to execute the tool to the corresponding MCP server in accordance with the MCP protocol specification (for example, via call_tool() method). An application may need to convert certain function call outputs from the LLM into an MCP-compatible request format.
• Response Handling : After the MCP server executes the tool, it returns the results in a standardized structure to the MCP host, which then provides the results to the LLM, which can use the results to generate the final response to the user.

To summarize the relationship between the two : It can be generally understood that function calling is the process of LLM "deciding what to do and issuing instructions", while MCP is the protocol and framework for "executing these instructions in a standardized manner and feeding back the results" .