Six common misunderstandings about MCP protocols: How many of them do you have?

MCP (Model Context Protocol) is very popular. How popular is it? According to the WeChat index in the past 30 days, the popularity of MCP is more than one-tenth of the DeepSeek large model that has already gone viral. This may not make people feel anything. If you compare it with Manus, which was popular some time ago, MCP is much more popular than the latter.

As a purely technical protocol, the MCP protocol has attracted so much attention, which shows that in the early stages of AI development, as a key infrastructure connecting large language models with external systems, it can indeed partially solve the usage bottlenecks of some scenarios in AI applications through standardized interfaces, efficient state synchronization and other designs in the field of model context management. However, just like some other emerging technologies in their early development, the role of MCP is now exaggerated, and there are some misunderstandings about the nature and capabilities of MCP in the industry. Therefore, this article will systematically comb through existing literature and industry practices, reveal the current three major aspects of MCP, six cognitive misunderstandings and their technical truths, and provide a theoretical framework for the correct understanding of this protocol.

1. Cognitive bias in protocol positioning

Myth 1: MCP requires native support for large models

Some practitioners believe that the implementation of MCP requires the language model itself to have specific protocol support capabilities. This view stems from a misunderstanding of the protocol architecture. In fact, MCP adopts a client-server architecture design, and its core function is to decouple the model from the external system through standardized interfaces. The protocol client serves as an intermediate layer, responsible for converting the model output into MCP specification requests, while the server handles specific data retrieval and tool calls. This design allows any language model that supports basic API calls to be seamlessly integrated into the MCP ecosystem without modifying the underlying model structure.

As shown in the figure above, when the traditional architecture is used on the left, AI applications need to be connected to each tool separately; after the MCP is introduced on the right, it can be uniformly accessed as a standardized interface. All kinds of tools (MCP Server) can be connected to the system, and AI applications (MCP Host/Client) can achieve efficient interaction through standardized protocols.

In terms of technical implementation, the MCP server exposes a unified endpoint through the JSON-RPC 2.0 protocol, and the model only needs to generate a JSON request that complies with the tool call specification to complete the interaction . Taking the GitHub code base operation as an example, the model triggers the MCP client through natural language instructions, which will automatically convert into tools/call endpoint requests, without the model having to understand the protocol details.

Myth 2: MCP is equivalent to the tool call protocol

Although tool invocation is an important function of MCP, simply equating it with the tool invocation protocol ignores its broader architectural value. The three core components of MCP - resource management, prompt engineering, and tool invocation - constitute a complete context enhancement system . The resource management module supports dynamic loading of structured data such as database records and API responses; the prompt engineering module allows predefined interaction templates; and tool invocation implements operation execution capabilities. This trinity design enables MCP to support full-scenario needs from simple data retrieval to complex workflow orchestration.

As mentioned earlier, the core architecture of MCP consists of three main parts: MCP Host, MCP Client and MCP Server . MCP Host is an application carrier (such as Claude Desktop, Cursor IDE), with built-in MCP Client to interact with the Server; MCP Client acts as a communication intermediary, actively queries the Server for available capabilities (tools/resources/prompts), and provides real-time feedback on task status and usage analysis; MCP Server provides three core capabilities: the tool layer supports automated API calls, the resource layer opens local/cloud data interfaces, and the prompt layer optimizes task execution efficiency through preset templates. The three communicate securely and bidirectionally through the Transport Layer. The communication process is as follows: Client initiates a request -> Server receives and replies to the capability list -> Both parties continue to synchronize the status through notifications . This standardized protocol approach can achieve capability sharing and contextual collaboration, thereby reducing the integration cost of AI systems.

Comparative analysis shows that traditional tool call protocols, such as the OpenAI plug-in system, only focus on the function execution link, while MCP achieves cross-session state maintenance and information sharing through mechanisms such as resource URI identification and context sampling. For example, in a continuous debugging scenario, the MCP server can maintain the historical context of code changes, avoiding the resource consumption of repeatedly transmitting the complete context in traditional solutions.

2. Cognitive Misalignment of Functional Boundaries

Myth 3: MCP can automatically improve model intelligence

Some corporate decision makers regard MCP as a "silver bullet" to improve the intelligence level of models. This perception confuses the boundary between protocol capabilities and model capabilities. Actual tests show that in the retrieval-augmented generation (RAG) scenario, MCP can only guarantee the accurate transmission of context data, while the retrieval quality still depends on the indexing strategy and similarity algorithm of the vector database . When there are defects in the basic retrieval system, the efficient transmission of MCP will amplify the risk of spreading erroneous information.

A case study showed that after an e-commerce customer service system was connected to MCP, although the response speed of work order queries increased by 40%, the error work order processing rate increased by 15%. The root cause was that the label errors in the original work order classification system were quickly spread to various interaction links through MCP ^[1] . This proves that the protocol itself cannot replace the quality optimization of the underlying system.

Myth 4: MCP is suitable for all deployment environments

The current industry has a tendency to blindly promote MCP cloud deployment, ignoring the security boundaries of protocol design. The MCP specification clearly requires that sensitive operations must be performed through a local sandbox, and its stdin/stdout communication mode is essentially optimized for a single-machine environment . In remote deployment scenarios, the lack of mature authentication mechanisms and traffic encryption solutions can easily lead to the risk of man-in-the-middle attacks. In 2024, a financial institution's MCP pilot project failed to configure TLS encryption, resulting in customer data being intercepted during transmission.

Analysis of the protocol specification shows that the security model of MCP is based on the assumption of a local trusted environment. Its access control relies on the host application's permission management system, and requires additional encapsulation of transport layer security when deployed across networks. This explains why early adopters such as Block and Apollo chose to deploy MCP servers in isolated network environments.

III. Cognitive Blind Spots at the Implementation Level

Myth 5: MCP eliminates the need for context management

Developers often mistakenly believe that they do not need to actively manage contexts after using MCP, which leads to memory leaks and performance degradation. Actual test data shows that if the MCP client process runs continuously for 24 hours and does not actively release the completed context, the memory usage will increase linearly at a rate of 2MB/minute. Although the protocol specification defines the context lifecycle interface, the specific release timing still needs to be determined by the developer based on business logic.

A typical case occurs in the continuous integration scenario: a development team did not call context.release() after the build task was completed, resulting in the continuous accumulation of build logs in memory, which eventually caused the container to overflow memory. This suggests that the standardized interface provided by MCP needs to be used in conjunction with the system resource management strategy and cannot be completely dependent on the automatic processing of the protocol.

Myth 6: MCP can ensure system security

Some architects regard MCP as a security solution, ignoring its potential risk of expanding the attack surface. The protocol supports local code execution, allowing malicious MCP servers to implant backdoor programs through tool call interfaces. A security incident exposed in March 2025 showed that an open source MCP server implementation had an unverified dynamic library loading vulnerability, which allowed attackers to execute arbitrary shell commands.

Security audits show that although the MCP specification includes a user confirmation process, the specific implementation depends on the security control of the host application. When the host application does not strictly perform operation confirmation, tool calls may bypass user authorization and execute directly. This requires that systems using MCP must establish a multi-layer defense system, including supplementary measures such as code signature verification and sandbox isolation.

Key insights into technological evolution

Synergistic relationship between MCP and existing technologies

In industry discussions, MCP and Function Calling are often opposed to each other. This either-or perspective does not conform to the law of technological evolution. Practical applications show that MCP can encapsulate a variety of tool calling specifications, including OpenAI's JSON Schema and Anthropic's Tool Use syntax. On Alibaba Cloud Bailian platform, the MCP server is designed as a compatibility layer that can simultaneously connect to tool calling interfaces of different manufacturers, proving that the protocol has good technical inclusiveness.

Performance test data shows that in a hybrid deployment scenario, the response delay of the Function Calling interface accessed through the MCP proxy only increases by 8-12ms, which is much lower than the 50ms overhead of the traditional gateway solution. This microsecond-level loss enables MCP to achieve unified management of the tool ecosystem without affecting the user experience.

Dynamic cognition of protocol evolution

Another common misconception is to view MCP as a static standard  . Since its release in November 2024, the protocol specification has undergone three major updates, involving core functions such as security models and streaming. The context versioning feature introduced in the March 2025 version effectively solves the state conflict problem in multi-branch development scenarios. This requires adopters to establish a continuous protocol tracking mechanism to avoid system compatibility issues caused by version lags.

Correction direction of practical cognition

Establish a Tiered Implementation Strategy

In response to the misunderstanding of MCP, it is recommended that enterprises adopt  a three-layer implementation architecture of "protocol layer-business layer-control layer" . The protocol layer strictly follows the MCP specification to ensure the reliability of basic communications; the business layer encapsulates domain-specific logic to avoid direct coupling of core business with the protocol; the control layer implements fine-grained permission management and audit tracking.

Improve the security awareness framework

Improving security awareness needs to start with threat modeling. It is recommended to use the STRIDE model to analyze MCP deployment scenarios, focusing on preventing the risks of privilege escalation and information disclosure in tool calls.

The current misunderstanding about MCP is essentially a misunderstanding that is bound to occur during a period of technological change. By systematically analyzing the protocol architecture, implementation cases, and evolution trends, we can establish a more accurate technical cognitive framework. In the future, with the support of the MCP 2.0 specification for federated learning and edge computing, as well as the continuous improvement of the security model, this protocol is expected to truly become the "digital nervous system" of the intelligent era, but realizing this vision requires industry practitioners to maintain rational cognition and continuous learning.