The meaning and function of MCP

During this period, as long as you read more about AI Agent, AI programming tools or automated workflows, you will basically encounter the word MCP.

When I first saw it, I actually didn’t take it seriously. There are too many new concepts that have emerged in the field of AI recently. Each one has a bigger name than the last, but not many of them have actually been implemented in engineering.

But the more I see MCP, the more I feel that it is not a new term that stays at the conceptual level. It is indeed filling a long-standing vacancy.

This gap is not complicated:

Just because the model can answer questions does not mean that the model can actually do things.

This problem will crop up as soon as the tasks start approaching real workflows. Viewing the warehouse, checking files, running commands, reading databases, opening browsers, and connecting to internal systems are not capabilities that will come naturally if the model itself is stronger in reasoning. There is always a layer in the middle: how to connect the model to the external environment.

MCP handles almost this layer.

1. The basic meaning of MCP

MCP is the abbreviation of Model Context Protocol.

More formally, it is a set of protocols that make it easier for models, AI clients, and external resources or tools to collaborate.

This sentence is correct in itself, but it is actually not easy to form a specific feeling when I see it for the first time.

To put it more directly, what MCP does can be understood as the following sentence:

** Organize the connection of models to external resources and tools into a more unified approach. **

The scope of “external resources and tools” here is huge, including:

• local files
• Git repository
• database
• Browser
• command line
• Internal system
• Knowledge base
• Third-party services

Of course, these capabilities can be integrated into AI products before, but most of them rely on proprietary adaptations written by each.

There is one way to connect files, another way to connect to databases, and another way to connect browsers. If you change the client, you have to do it all over again. When there are few tools, it is still acceptable. When there are too many tools, problems arise:

• Access methods are becoming increasingly fragmented
• Different clients do the same thing repeatedly
• Parameters, permissions, and return structure styles are different
• Subsequent maintenance costs continue to rise

This is what MCP is all about. It is sorting out a type of ability that originally existed but has always been fragmented.

2. Background of the emergence of MCP

If AI is only used as a chat tool, MCP’s presence will not be very strong.

Asking questions, summarizing, writing a piece of code, changing a piece of copy, most of these tasks remain at the level of “generating content”. Context is usually pasted manually, and tool calls are not strictly necessary.

But now many AI products are no longer satisfied with this layer.

The direction is becoming increasingly clear, and the goal is to allow AI to enter real workflows, at least to participate in the following things:

• Look directly at the project code
• Find relevant documents yourself -Execute command
• Read test results
• Check page status
• Connect to database or knowledge base
• Continue to advance tasks based on the results

Once the goal becomes like this, the original set of scattered tools begins to seem insufficient.

What really needs to be dealt with is mainly the following things:

• Whether the access is unified
• Whether the expansion is smooth
• Are permission boundaries clear?
• Whether there is long-term stable collaboration between the client and the tool

The emergence of MCP is essentially because this type of demand began to explode intensively.

As AI products move from “answering questions” to “participating in execution,” sooner or later the access layer will become a problem that must be dealt with head-on.

3. The level of ability supplemented by MCP

The most valuable aspect of MCP is not in the model parameters or the “tuning tool” at the publicity level, but in that it can reduce the problem of the model guessing behind closed doors in the real environment.

There is a common experience in engineering:

The results generated by the model look decent, the terminology is correct, and the structure is complete, but when it comes to actual projects, it starts to deviate. The reasons for bias are often not mysterious, they are nothing more than these:

• Can’t see the real directory structure
• I don’t know how to run the script
• Not sure where the configuration file is
• Can’t get running status
• Unable to access database or logs
• Don’t know what resources are available in the current environment

Without this information, the model can only guess.

When the guess is right, the result looks okay; when the guess is wrong, the problem is often not easy to spot at first glance because it still looks complete on the surface.

The value of MCP lies precisely in its attempt to transform this process of “relying on guessing and filling in environmental information” into “obtaining real context and tool capabilities through a unified method.”

From this perspective, MCP is more like a layer of infrastructure rather than an isolated concept.

4. Three perspectives for understanding MCP

If you don’t want to look at the details of the agreement right away, MCP can be understood from three perspectives.

1. A unified socket

This is the easiest way to form an intuitive understanding.

It turns out that different tools, different systems, and different data sources are like plugs of different specifications when connected to the AI ​​client. You have to re-fit the adapter every time you connect one, which will definitely become more and more messy in the long run.

MCP is more like pushing for a unified set of sockets.

It may not be able to smooth out all the differences, but at least it can make the matter of “how to connect” less wild.

2. A translation layer between models and tools

The model is good at understanding language, but it does not naturally understand the different styles of private interfaces of each system.

The file system has file system rules, the database has database rules, and browser debugging is a completely different set of things.

An important thing MCP does is to organize these capabilities into a form that is easier to describe, easier to discover, and easier to call, such as:

• What resources are currently available?
• What operations can be done
• What parameters are required when calling?
• What structure will the return result be?

In this way, the cost of collaboration between clients and tool providers will be much lower.

3. A dynamic context channel

Context was often understood as “a piece of text posted to the model”.

But the context of real work goes beyond that.

Project files, log output, database records, browser page status, Git changes, and command execution results all belong to context, and they are also dynamically changing contexts.

One of the important meanings of MCP is to give this type of context the opportunity to be obtained through standard methods, instead of relying entirely on manual copy and paste.

5. The actual role of MCP

MCP can easily appear abstract if we only talk about definitions. To truly see its value, we still have to go back to the actual usage scenario.

1. Make it easier for AI to connect to real tools

This is the most direct level of value.

When an AI client supports MCP, it will naturally gain access to the following capabilities:

• Read and write files
• Check warehouse status
• Query database
• Debug browser pages
• Get command output
• Access the knowledge base
• Connect to external services

At this time, the role of AI will change.

It no longer just “gives advice” but begins to have the ability to “participate in completing tasks.”

2. Reduce duplication of work in tool access

A lot of integration work in the past was essentially hard-coded one-to-one:

• One client adapts to one service
• Change to another client and adapt again
• The same capability is rewritten repeatedly in multiple products

This method works well in the short term, but is very wasteful in the long term.

If more and more tools can expose capabilities in a way similar to MCP, both tool providers and clients will feel a lot easier:

• Tool capabilities are easier to reuse
• The client expands new capabilities more smoothly
• The entire ecosystem will not be reinvented every time there is an additional tool.

3. Make context acquisition more natural

In complex tasks, one of the most attention-consuming things is to repeatedly add context.

Where is the project, which directory is important, which log is critical, which command is correct, which file can be changed, and which file cannot be touched. If these information are interpreted manually every time, the cost is very high and it is easy to leak.

When these abilities can be accessed by AI in a unified way, the whole process will be much smoother.

Such improvements may not be as noticeable as model upgrades, but they have a significant impact on real mission completion rates.

4. Make Agent more like a real executor

The word Agent is used very loosely nowadays, but a truly practical Agent must possess at least a few things:

• Observe the environment
• Get information
• call tool
• perform actions
• Continue to advance based on the results

If tool access is always fragmented, private, and difficult to expand, the Agent can easily stay at the level of “talking in steps”.

What MCP adds is the most critical basic layer for Agent to move from “looking like it can do things” to “actually being able to do some things”.

5. Easier to manage permissions and capabilities

Once AI starts to connect to real systems, permission issues cannot be avoided.

For example:

-Which directories are allowed access

• Which commands can be executed
• Which data is allowed to be read
• Which operations must be manually confirmed
• Which abilities can only be opened under specific circumstances?

If access methods are all improvised, permission boundaries will also fall apart.

Once access methods begin to be standardized, it will be easier to integrate capability management and authority management into the same system.

This is especially important for enterprise scenarios.

6. The difference between MCP and ordinary API

This question is natural, because on the surface, the final implementation is to adjust various capability interfaces.

I prefer to understand the relationship between API and MCP this way:

API mainly organizes capabilities from the perspective of service providers.

What it cares about is:

• What interfaces does the service expose?
• How to pass parameters
• what results are returned

MCP focuses on how AI is organized when using these capabilities.

What it cares more about is:

• How does the model discover available resources?
• How does the client understand these capabilities?
• How tools can be exposed to AI in a more consistent way
• How to put permissions and call boundaries into the same system

So there is no conflict between the two.

API is still part of the underlying capabilities, and MCP is more like a layer of organizational standards in AI scenarios.

7. Scenarios where MCP can more easily embody value

The value of MCP is not particularly strong in ordinary chats. It is usually reflected in task-based scenarios.

1. Programming Assistant

This is one of the most typical application scenarios.

A truly useful programming assistant usually not only needs to be able to explain the code, it must also be able to access:

• Warehouse files
• Build script
• test results
• Terminal commands
• Git status
• Page execution results
• Log output

If all these things rely on private adaptation, the system will become increasingly heavy as it expands.

If there is a more unified access method, it will be easier for the programming assistant to upgrade from a “code question and answer tool” to a “task collaboration tool.”

2. Enterprise knowledge assistant

Internal corporate knowledge is often not in one place:

• There is a part in the file system
• There is a part in the database
• There is a part in the reporting system
• There are also parts in work orders and CRM

In this scenario, the real difficulty is usually getting the system to get the correct context stably.

The place where protocols such as MCP are most suitable to play a role is precisely in this multi-system, multi-data source, and multi-authority boundary environment.

3. Automated workflow

Daily summary, exception checking, report generation, reminder sending, status synchronization, these tasks are essentially a combination of “reading multiple systems + making judgments + adjusting actions”.

In this scenario, the more unified the access layer is, the lower the orchestration cost will be.

The value of MCP will become more and more obvious as the complexity of the tool chain increases.

8. Reasons for the rising importance of MCP

The reason is not complicated. AI products are moving from “answering” to “executing”.

When answering only, the focus mainly falls on the quality of model generation.

When it starts to move toward execution, the system’s focus will change significantly, and it will start to fall more on these issues:-How many tools can be connected?

• What resources can be accessed?
• Is it possible to obtain real environment information?
• Can actions be performed within the scope of authority?
• Can you close your boundaries when something goes wrong?

These issues are essentially related to connectivity.

The model itself is of course still important, but once you enter the workflow, the importance of connectivity increases very quickly.

MCP is right at this point of change.

9. The significance of MCP to developers

The MCP deserves attention, and the focus doesn’t have to be solely on the details of the agreement.

What’s more worth watching is the trend it represents:

In the future, many software products may not only provide Web UI and ordinary APIs, but also provide a layer of AI-oriented access methods.

This means several things will slowly happen:

• Tool products will have a new distribution channel
• The AI client will regard “what it can accept” as one of its core capabilities
• The scalability of the Agent system will increasingly rely on this type of unified access layer

From an engineering perspective, MCP actually touches a very hard problem:

**If AI wants to enter the real workflow, the matter of connecting tools, resources, and environments must sooner or later move from a fragmented state to a standardized stage. **

The MCP is at least taking the issue seriously.

Summary

In a shorter way, MCP can be understood like this:

**MCP makes it easier for models to stably connect to external resources and tools. **

To expand a bit, these are the things:

• It deals with how the model obtains context, discovers resources, and calls tools
• It organizes the originally fragmented access methods
• It won’t replace the API, but it will change how AI is organized to use these capabilities
• It is especially important for scenarios such as programming assistants, enterprise assistants and automated workflows

The next big thing for AI applications is not just the model itself, but also connection capabilities, context quality, and execution stability.

MCP falls squarely on this line.