What is Model Context Protocol (MCP)? How to build AI Agents?

Model Context Protocol (MCP) is a framework that facilitates communication and interaction between Large Language Models (LLMs) and external tools or knowledge sources. It provides a standardized way for LLMs to request specific information, trigger actions, or access data from these external systems, enriching their knowledge base and enabling them to perform more complex tasks beyond their pre-trained capabilities. Think of it as a translator allowing LLMs to "talk" to the outside world in a structured and efficient manner, enabling them to leverage tools like search engines, APIs, databases, and specialized software.

Connecting AI agents with databases and APIs, while focusing on Model, Compute, and Persistence (MCP), means choosing the right language models (LLMs) that are adjusted for working with data (Model), providing enough computing power for the agent to handle data, run queries, and manage API calls effectively (Compute), and setting up a storage system to keep the agent's knowledge, collected data, and activity records, using vector databases for quick searches and regular databases for organized data management to make sure data is reliable and easy to access over time (Persistence).

Below is from IBM Technology showing its concept with a diagram:

MCP (Model Context Protocol) is a new open-source standard designed to simplify the connection of AI agents to various data sources like databases, APIs, and local files. It consists of a host (e.g., a chat app or code assistant) with one or more clients, an MCP protocol, and one or more servers that act as intermediaries between the agent and the data sources. The host queries the server for available tools based on the user's request, then uses the LLM to choose which tools to use, calls the appropriate MCP server to execute the tool, and returns the results to the LLM to get the final answer. MCP aims to standardize the way agents access and utilize external data, making agent development more efficient and flexible. Developers building AI agents or providing services to agent developers should consider implementing and utilizing MCP.