What Is MCP (Model Context Protocol): Why It Makes AI Tool Invocation Standardized

Over the past period, MCP has become one of the most frequently discussed keywords in the AI field. Its significance is not in being the first to give models tool-calling capabilities, but in its attempt to use a more unified approach to define the connection relationships between models and external tools, data sources, and business systems.

In one sentence:

MCP is a protocol that aims to progressively standardize the way models and tools collaborate.

The core problem it addresses is not “can AI call tools” but “can different models, different platforms, and different tools understand each other in a more consistent way.”

1. Before MCP: What Were the Problems with AI Tool Invocation

Before MCP appeared, AI calling tools was certainly achievable, but the implementation was often highly fragmented.

Common situations included:

• Connecting a model to Slack required a separate custom solution
• Connecting the same model to Notion required yet another solution
• When the platform changed, tool integration methods often had to change as well
• When models were upgraded or switched, connection code frequently needed re-adaptation

This led to several direct problems:

1. Almost every connection required custom implementation
2. The more tools, the higher the integration complexity
3. Switching models or platforms significantly increased migration costs
4. Model capabilities iterated quickly, but the connection layer remained fragmented

The real bottleneck many teams encountered was not in model capability itself but in the connection layer between models and business tools.

2. What MCP Is Actually Standardizing

What MCP standardizes is not any specific tool, but the interaction pattern between models and tools.

More specifically, it attempts to answer these questions:

• How does a model know what a tool can do
• How does a tool describe its own capabilities, parameters, and return structure
• How does a model initiate an invocation
• After a tool returns results, how does the model continue processing

Without a unified protocol, these matters were typically handled by each platform, each plugin, and each developer through their own conventions. The value of MCP is:

Bringing the previously scattered private conventions across different systems back to a more generalizable protocol layer.

3. Why MCP Is Often Compared to “USB-C for the AI World”

Many people compare MCP to USB-C, and this analogy is very intuitive.

The value of USB-C is not that it invented monitors, keyboards, or hard drives, but that it unified the connection method, making it easier for different devices to form a standard interface.

The significance of MCP in the AI world is similar:

• It is not inventing email systems
• Not inventing databases
• Not inventing search, documents, and calendar tools

Rather, it enables these external capabilities to describe themselves, expose themselves, and be invoked in a more consistent manner when facing models.

From this perspective, the true significance of MCP lies not in any single tool but in the evolution of the entire connection ecosystem.

4. Why MCP Makes Tool Invocation More Standardized

Whether tool invocation is truly “standardized” depends not on whether it can be invoked, but on whether the invocation behavior is sufficiently predictable, portable, and reusable.

The value of MCP is primarily reflected in four areas.

1. Tool Capabilities Can Be Described More Clearly

For a tool to be invoked by a model, it needs to clearly state at minimum:

• What it is
• What it can do
• What input parameters it requires
• What kind of results it will return

When this information can be expressed through a unified approach, the cost for models and platforms to understand tools drops significantly.

2. Coupling Between Models and Tools Decreases

In the past, many tool invocation approaches were tightly dependent on specific platforms.

Something that works on one platform today might require re-integration on another; a model that can call it today might need separate adaptation after switching models.

Once the protocol tends toward unification, the relationship between models and tools becomes more like “collaborating through standard interfaces” rather than “deeply binding to each other.”

3. Tool Integration Gradually Shifts from an Engineering Problem to a Configuration Problem

This does not mean engineering work disappears, but rather that many places previously requiring hand-written glue code may increasingly transform into:

• Describing tool capabilities
• Configuring authentication methods
• Setting invocation boundaries
• Controlling permissions and behavior

When connection costs decrease, the truly important question shifts from “how to connect” to “why connect, which ones to connect, and how to govern.”

4. Cross-Platform Reuse Capability Improves

If a tool exposes capabilities through MCP, its value is no longer limited to use within a single platform but has the opportunity to be integrated by multiple MCP-supporting systems.

This will change how the entire ecosystem is built:

Platforms will no longer need to each maintain entirely independent plugin systems and may instead share an increasing number of capabilities through the protocol layer.

5. What MCP Means for Platforms Like Dify

For AI application platforms like Dify, MCP’s value is especially significant.

Because Dify itself sits in a very critical position:

• One side connects to models
• The other side connects to knowledge bases, workflows, tools, and business systems

Without a unified protocol, platforms often need to rely on a large number of plugins or maintain different integration methods for different tools. The change MCP brings is:

• Application capabilities built with Dify can be further exposed externally
• External MCP Servers can also be integrated into Dify’s Agent or workflow processes
• Tool invocation begins transitioning from “platform-proprietary capability” to “protocol capability”

This makes Dify not just an application building platform but also more easily a part of the enterprise AI connection ecosystem.

6. What Is the Difference Between MCP and API

Many people, when first encountering MCP, ask:

“How is this different from an API?”

The two are related but not the same.

API is more like the capability itself

For example, weather API, email API, database API – they are essentially interfaces to system capabilities.

MCP is more like the collaboration method when models access these capabilities

It focuses on:

• How models understand tools
• How tools expose capabilities to models
• How parameters and return results are described uniformly
• How the entire invocation process is organized more consistently

Therefore, it can be understood this way:

API is the capability exposure of the tool itself; MCP is a more standardized interaction protocol between models and tools.

7. Changes MCP May Bring

If MCP continues to develop, the most direct changes will typically manifest on three levels.

1. Model Competition Will Partly Shift to Connection Capability Competition

The future competition may not just be about “which model is stronger” but also:

• Which is easier to connect to enterprise systems
• Which is better at orchestrating external capabilities
• Which is more suitable to serve as an execution node in workflows

2. The AI Tool Ecosystem Will Become More Modular

The boundaries between models, platforms, tools, and business systems will gradually become clearer.

This means enterprises will not be forced to be entirely locked into a single closed-loop product but can organize capabilities in more flexible ways.

3. Design Problems Will Matter More Than Wiring Problems

As connection methods become increasingly standardized, what truly differentiates will no longer just be “can it connect” but:

• Which tools to connect
• In what scenarios to invoke them
• How much authority to give the model
• Which steps still retain human control

In other words, the focus of AI applications will increasingly shift from “technical connectivity” to “workflow design and governance design.”

8. MCP Is Not a Silver Bullet

Of course, MCP does not mean all problems automatically disappear once the protocol is adopted.

It is still constrained by at least the following real-world conditions:

• Whether the tool side truly exposes capabilities according to the standard
• Whether the platform side has sufficiently good MCP support
• Whether permission control is designed clearly
• Whether the enterprise is willing to connect critical systems to such an open invocation framework

Furthermore, standardization does not equal security, nor does it mean governance is automatically complete.

Even when a tool already has standardized invocation methods, enterprises still must be clear about:

• Who can invoke it
• In what scenarios
• What can be read, what can be written
• How to handle invocation failures

Therefore, MCP addresses the problem of “standardizing connection methods,” not the problem of “all integration risks automatically disappearing.”

Conclusion

MCP is worth paying attention to not because it was the first to teach AI to call tools, but because it is progressively pushing what was previously a highly privatized, one-off adaptation connection model toward a more standardized stage.

In the long run, its true significance may not just be “connecting faster” but making the entire AI ecosystem closer to a composable system:

• Models are responsible for understanding and generation
• Tools are responsible for capability execution
• Platforms are responsible for organization and governance
• Protocols are responsible for connecting them all more stably

This is also why MCP is especially important today. It is not just a technical term – it is a very critical infrastructure-level shift in AI’s journey from “being able to chat” to “being able to connect to systems.”