What is MCP? Integrate AI Agents with Databases & APIs

Summary

The video introduces the Model Context Protocol (MCP), an open-source standard for connecting AI agents to data sources like databases, APIs, and local files. MCP consists of three core components: the host, client, and server. It explains how these components interact, with the MCP host and client requesting tools from MCP servers, which in turn connect to various data sources. The video showcases how MCP can be applied in practical scenarios, like a chat app or code assistant, to fetch answers by integrating tools through the protocol. Developers are encouraged to explore MCP for building AI agents and enhancing their applications.

Takeaways

• 😀 MCP (Model Context Protocol) is an open-source standard for connecting AI agents to data sources such as databases and APIs.
• 😀 The key components of MCP are the host, client, and server, with the host being the central element.
• 😀 The MCP host can be an application (like a chat app) or a code assistant in an IDE.
• 😀 MCP servers connect to various data sources like relational databases, NoSQL databases, APIs, and local files.
• 😀 MCP uses a transport layer protocol to facilitate communication between the host, client, and server.
• 😀 An MCP host can connect to multiple MCP servers, which can handle requests from the host or client simultaneously.
• 😀 The protocol enables seamless tool usage between the MCP host and server when interacting with data sources.
• 😀 In practice, a chat app can act as the MCP host, and a question from a user will prompt the host to connect to a large language model (LLM).
• 😀 The LLM assesses the tools available and advises the MCP host on which tool to use to retrieve the necessary data.
• 😀 The MCP server executes requests such as querying databases or APIs, and sends back the response to the LLM for final processing.
• 😀 MCP protocol provides a flexible and scalable way for agents to access various data sources, making it ideal for building AI agents.

Q & A

What is MCP and what does it stand for?

MCP stands for Model Context Protocol. It is an open-source standard used to connect AI agents to various data sources, such as databases, APIs, and local files.

What are the main components of MCP?

The main components of MCP are the host, the client, and the server. These components work together to enable the connection between AI agents and data sources.

What role does the MCP host play in the protocol?

The MCP host is responsible for connecting to the MCP server and managing communication between clients, such as applications like a chat app or code assistants, and data sources.

Can an MCP host connect to multiple MCP servers?

Yes, an MCP host can connect to multiple MCP servers, allowing for flexible and scalable connections to different data sources.

How does the MCP protocol function as a transport layer?

The MCP protocol acts as a transport layer, facilitating the communication between the MCP host/client and MCP servers, ensuring tools are connected and data is retrieved efficiently.

What are the types of data sources that MCP servers can connect to?

MCP servers can connect to a variety of data sources, including relational and NoSQL databases, APIs, and local file systems. They can also interface with code for applications like IDE assistants.

In the example of a chat app, how does the MCP protocol work when a user asks a question?

When a user asks a question, the MCP host connects to the MCP server, which identifies the tools available. These tools are then relayed to a large language model, which determines how to retrieve the necessary data from sources like APIs or databases.

What is the process once the MCP host and client know which tools to use?

Once the tools are identified, the MCP host calls the appropriate MCP server, which executes queries to data sources like databases or APIs. The results are then sent back to the large language model, and finally to the user.

Why should developers consider using the MCP protocol when building AI agents?

Developers should consider using the MCP protocol because it simplifies the connection between AI agents and diverse data sources, streamlining the process of data retrieval and integration into applications.

Can the MCP protocol be beneficial even if you're not directly building agents?

Yes, even if you're not directly building agents, the MCP protocol can still be valuable as clients might be developing agents, allowing for seamless integration with various data sources.