Understanding the Model Context Protocol (MCP) for Agentic AI
The Model Context Protocol (MCP) is more than just a tool for enhancing desktop applications. It offers a broader vision for developing professional agentic AI applications, especially within enterprise environments. This article will delve into the workings of MCP, exploring its architecture, functionality, and benefits.
How LLMs Work and the Need for Agentic AI
Traditionally, Large Language Models (LLMs) receive a prompt and generate a response in the form of words. While this is sufficient for some tasks, agentic AI aims to go further by enabling AI to take actions and create real-world effects. This requires LLMs to interact with tools and access up-to-date and comprehensive information beyond their core foundation model.
Expanding the LLM's Knowledge Base
To enable agentic AI, LLMs need access to external resources. This can be achieved using techniques like Retrieval Augmented Generation (RAG) to incorporate enterprise data. Regardless of whether RAG is used, the ability to access various resources like files, databases, and even Kafka topics is crucial. These resources provide the agent with the data it needs to make informed decisions and take appropriate actions.
MCP Architecture: Host Applications and Servers
MCP introduces a specific architecture to facilitate agentic AI. It involves building an agent as a microservice, referred to as the "host application." The host application uses an MCP client library to create a client instance. Complementing this is the MCP server, which can be either an existing server offering agentic functionality or a custom-built server.
The Role of the MCP Server
The MCP server acts as a central repository of tools, resources, prompts, and capabilities that it makes available to the outside world. It exposes these functionalities through well-defined RESTful endpoints, as described by the MCP specification. A crucial element is the "capabilities list," which informs the host application about the available tools, resources, and prompts.
Communication Between Client and Server
The communication between the MCP client and server can occur through standard IO (pipes) for local processes. However, HTTP and Server-Sent Events using JSON RPC messages are preferred for a more robust setup. This setup includes mechanisms for client announcement, server discovery, and asynchronous notifications from the server to the client.
Workflow Example: Appointment Scheduling
Consider a service for scheduling appointments, such as meetings or dinner reservations. This requires several tools and resources:
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Tools: Calendar API integration, restaurant reservation system.
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Resources: User's calendar information, availability of other attendees, list of local restaurants and coffee shops.
Instead of embedding this functionality directly into the agent, MCP allows these resources to be accessed through a server. The workflow would involve the user providing a prompt (e.g., "I want to have coffee with Peter next week").
- The host application interrogates the capabilities of the MCP server.
- The host application uses the LLM to identify required resources from the server.
- The client requests these resources from the MCP server.
- The LLM uses provided resources to suggest tool invocations to the host application, such as calling an API to create the calendar invite.
Advantages of MCP
MCP offers several key benefits for building agentic AI applications:
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Pluggability: Easily integrate new tools and resources without modifying core agent code.
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Discoverability: Agents can discover and utilize available functionalities through the capabilities list.
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Composability: Servers can themselves be clients of other servers, enabling complex workflows (e.g., consuming data from a Kafka topic via a Confluent MCP server).
MCP is a foundational element for building truly agentic AI applications within enterprises. By providing a standardized way to access tools and resources, it paves the way for more sophisticated and capable AI systems.