The integration of artificial intelligence into enterprise systems is a top priority for businesses seeking a competitive edge. However, this process is often fraught with challenges, including data silos, legacy systems, and the need for costly custom-built connectors.
According to a McKinsey report from 2025, while 92% of companies plan to increase their AI investments over the next three years, only 1% of leaders consider their companies “mature” in AI deployment, meaning it’s fully integrated into their workflows.
This significant gap highlights a major barrier to widespread AI adoption. The Model Context Protocol (MCP) was introduced to address this exact issue.
By creating a standardized, open-source framework, MCP aims to simplify how AI models communicate with and utilize data from various applications and systems, paving the way for more seamless and effective enterprise-wide integration.
KEY TAKEAWAYS
- MCP is an open-source, standardized framework that enables AI models to communicate with applications and data sources.
- It was developed to solve the problem of having to build unique, custom connectors for every AI model and application, which was a major roadblock to scaling AI.
- An MCP client in the AI model sends standardized requests, and an MCP server in the application or database translates these requests into its native language while enforcing security rules.
- MCP is crucial for the development of “agentic” AI, which can use multiple tools and data sources to make decisions and take actions with minimal human intervention.
- Its uses include automating complex workflows, giving AI secure access to file systems and databases, and integrating AI with multiple applications within a single enterprise system.
- While MCP simplifies integration, its open-source nature presents security risks, such as prompt injection and malicious servers.
MCP (Model Context Protocol) is an open-sourced and standardized framework defining how an AI model and applications or data sources talk to each other.
MCP needs an MCP client in its code for the AI model or solution (such as ChatGPT or Clause) to function. This is how it acknowledges and uses MCP communication rules.
On the other hand, the data source or application must have an MCP server that controls what part of the app or data source the AI model can access.
Even if you want to interact with an AI solution to an App’s API, there must be a ‘translator’ layer (the MCP server) that helps facilitate app language to MCP language translation and vice versa for the setup to work.
Essentially, MCP is similar to the agreed-upon sentence structure and grammar, and both sides need to understand this for an AI model to interact with a data source or application effectively.
As previously stated, the primary reason MCP was created was to solve the problem of needing to create unique bridges for AI applications and solutions. It is a bridge that’s put together once and open-sourced for AI and app developers to use. Here’s what you can use MCP for:
You had to use tools such as Make or Zapier to automate workflows before the launch of MCP. Sometimes, you had to manually connect App APIs to facilitate communication or data transfer between Apps.
So far, MCP has drastically altered how you go about automating workflows. As long as an AI solution is MCP-allowed, you can connect it with other MCP-enabled Apps. You give the AI a goal, and it talks to specific App MCP servers to accomplish it.
For example, if you want an MCP-enabled AI version to fetch data from the web and update an internal database, you are required to connect it to a web scraper MCP server and the database’s MCP server. Once you set up the connection logic, the AI can operate independently.
INTERESTING TIDBIT
The infographic below shows various AI challenges addressed by MCP.
While generative AI can perform several tasks like translating text, writing blog posts, or summarizing text, task implementation is mostly limited to prompt requirements and training data.
To lessen the impact of the restriction, you can use MCP to grant access to your file system to an AI program like ChatGPT. You set up an MCP server to represent the file system to make this effective.
The provider should know how to cope with requests like searching, listing, writing, and reading files. It should also be able to enforce certain security measures, commanding what files the AI can and can’t access.
The precise file system architecture or APIs are not required to be known by the AI’s MCP client. It has just required to acknowledge the standard MCP messaging format to communicate with the file system.
There are MCP servers for popular databases such as MySQL, MongoDB, and PostgreSQL. You can connect different AI solutions to these servers because they comprehend the MCP protocol and the native query language or structure of the database.
The AI solution doesn’t necessarily have to know a database’s internal structure directly. The MCP client deployed within the AI’s code will allow it to send standardized MCP query requests to a specific MCP-enabled database.
After that, the requests are converted into legitimate database queries by the database’s MCP server. You can interface one AI solution with multiple databases, instructing it to access a broad range of data elements and use them to accomplish a set goal.
Moreover, you can set up the AI to manage a specific database. As long as you customize the database’s MCP server appropriately to enforce security measures, you can direct an AI model to filter results, update data, search records, or insert new entries.
Enterprise systems are a combination of multiple applications. Some of these applications run independently, whereas others are integrated through APIs.
Every application must have its own MCP server to connect AI tools to enterprise systems. If you are connecting AI to an App’s API, you must verify that the MCP server in place can ‘translate’ the API’s output to MCP’s standard protocol and vice versa.
You can alternatively configure the MCP servers to implement role-based permissions and log actions for auditing. This additionally makes AI deployments in enterprise systems significantly simpler to govern and safer.
Since the enterprise tools follow a predefined MCP format, it is easier for AI to trigger actions, pull data, or update multiple systems. However, always keep security systems up to date and examine every outsourced MCP server before using any.
Our AIs can now make decisions and act with little to no human involvement by utilizing a variety of data sources and tools, thanks to MCP.
An agentic AI can figure out what tool to use, in what order, and how to manage the results. It rarely waits for human prompting to take action, such as traditional AI does. It can act, plan, and adapt based on evolving goals.
For example, agentic AI solutions like CrowdStrike Falcon are relied upon to automatically detect and neutralize cyber threats. In order to accomplish this, they actively search the system for malicious activity. Additionally, once they find a malicious activity, they put an end to it with minimal human oversight.
FUN FACT
Before MCP, most AI models were like goldfish — they forgot your context almost instantly. MCP is like giving them a memory backpack, so they can carry important details with them across apps and conversations.
MCP is a standardized framework that illustrates how AI tools and applications interface.
You can connect it to another MCP-integrated application or data source if the AI tool you are using is MCP-enabled. This implies that you don’t always need to create a unique connector for each AI model and application. However, there’s a catch!
MCP being open-source poses some risks, especially when it comes to cybersecurity.
Some well-known cyber threats in the MCP space involve prompt injection, token theft, and malicious servers. So, before you use a third-party MCP server, always review its code or scan the server for malware.
Ans: MCP is a standard that allows AI models and applications to share and use context in a structured way, making interactions more accurate, consistent, and useful.
Ans: It was developed to sole the problems of AI models losing context between conversations or across different tools, which often leads to repetitive questions or incomplete answers.
Ans: MCP itself doesn’t automatically store anything, it’s just a protocol. How data is stored or managed depends on the specific app or service using it.
