Understanding Agentic AI through MCP (Model Context Protocol)

                                                 art by: J. Sridharan, Dubai

Agentic AI Orchestrator Protocols in Simple Terms

Earlier in Nov 2024, Anthropic open sourced MCP (Model Context Protocol) - an open standard that enables developers to build secure, two-way connections between their GenAI applications and the tools and data sources. MCP is an open-source protocol that simplifies connections between AI systems and various data sources to help deliver faster innovation in context-aware Agentic AI applications. 

What are Agentic AI Orchestrators

Agentic AI is seen as the second big evolution of GenAI, and Agentic AI orchestrators are seen to be the enablers of this evolution. As LLMs continue to evolve providing multi-modal and extended built-in capabilities - in bare terms, LLMs are good in the predictability of the next word, which makes them great poem reciters and essay writers or converting text to visuals or translating languages and many more standalone tasks.  But there is a fundamental limitation – LLMs cannot take any intelligent action unless they are integrated with tools and data sources – for example: if you ask an LLM any information that it was not trained on (for example: current stock market trends) - unless it is connected to a web search engine – it cannot provide you with an accurate answer.

 In the GenAI application space, in terms of standardizing the communication protocols we are still in an era that can be compared to the pre-Rest API era - Just like how RESTful APIs accomplished simplified, standardized communication between client and server applications, there is a significant opportunity to standardize communication protocol between LLMs, tools and data sources. Consider a simple GenAI application built with a single model (a single Agentic AI application) – let’s say - a personal travel assistant – helping you to not only plan but do the bookings for a holiday - this agent ideally must fetch details from multiple sources to fulfil the task – Google maps to determine the place of interest, an OTA such as Expedia and other providers such as Booking.com, execute your credit card etc. Without a standardized way of connecting to the tools, building GenAI applications though not impossible, is very engineering intensive.

 In simple terms, building a GenAI Agentic application has 4 components (in short TATA)

• Task to accomplish,
• The model/s or Agent/s,
• Tools it needs to accomplish the task
• Answer the agent provides.

Without standardized protocols, the following are few of the key challenges to accomplish TATA.

• Custom built implementations required significant engineering effort to plumb tools and data sources. In addition, consider the re-engineering efforts when sources change.
• When connecting multiple agents, inconsistent prompt logic with different methods for accessing and federating tools and data will provide inefficient answers.
• The Scale problem - "n times m problem" - where a a large number of client applications interacting with a mesh of servers and tools will result in a complex web of integrations, duplicity, each requiring specific integration efforts.

MCP allows AI Agents to use tooling, resources and even prompt libraries in a standardized manner, thus extending the Agentic AI capabilities significantly to build more meaningful GenAI applications.

Just to keep the MCP architecture simple, MCP uses a client-server architecture, primarily at a high level, the key components being an MCP client, the MCP server and the MCP communication protocol. Developers expose their data through lightweight MCP servers. For example, Anthropic has released a few popular MCP server codes already such as for Google maps, or Slack. By connecting to these MCP servers, you can easily build an Agentic AI MCP client following the MCP protocols.

MCP Architecture

MCP uses a client-server architecture that contains the following components and is shown in the following figure:

• Host: An MCP host is a program or AI tool that requires access to data through the MCP protocol, such as Claude Desktop, an integrated development environment (IDE), or any other AI application.
• Client: Protocol clients that maintain one-to-one connections with servers.
• Server: Lightweight programs that expose capabilities through standardized MCP, allows access to data sources tools and even prompt libraries.
•  Local data sources: Your databases, local data sources, and services that MCP servers can securely access.
• Remote services: External systems available over the internet through APIs that MCP servers can connect to.

MCP, thus by providing an open-source protocol and a universal standard that simplifies connections between AI systems and various data sources - will deliver agility in building efficient and context-aware AI applications. Consequently, this will enable AI agents to autonomously perform complex tasks.

The success and widespread adoption of protocols like MCP depends upon industry participation and standardization efforts on interoperability and portability, and adherence to common standards, allowing AI applications to operate across different platforms and jurisdictions, crucial for global companies and responsible AI. 

MCP will help build trust by ensuring AI systems are transparent, reliable and secure. The clarity provided by the MCP protocol guidelines will reduce compliance complexity, will lower barriers to innovation and will foster faster development of AI products.