Model Context Protocol Overview

Summary

The meeting provided an in-depth explanation of the Model Context Protocol (MCP) and its significance in building agentic AI applications, especially in professional and enterprise settings.
Key distinctions were made between basic LLM functionality, RAG patterns, and the MCP’s host/client-server architecture.
The discussion illustrated MCP's value for pluggability, discoverability, and composability of tools and resources, broadening AI agent capabilities beyond simple desktop enhancements.

No explicit action items or owners were assigned in the transcript.

Highlighted that most discussions around agentic AI narrowly focus on desktop applications, missing the broader potential of MCP for professional use.
Clarified the limitations of LLMs: they return only words, not actions, which is where agentic AI and MCP become essential.

Explained that agentic AI systems require the ability to invoke external tools and access up-to-date or extended information beyond the base model.
RAG (Retrieval Augmented Generation) is still valuable in enterprise, but MCP enables structured access to a wider range of resources (files, binaries, databases, Kafka topics, etc.).

Described the architecture involving a host application (client) that communicates with an MCP server via libraries and specified endpoints.
The server exposes tools, resources, prompts, and capabilities to clients over RESTful endpoints, using either standard IO for local processes or HTTP/JSON-RPC/Server Sent Events for broader communications.
The protocol supports asynchronous notifications and discovery of available capabilities.

Walked through an example where an agentic application schedules appointments, requiring integration of calendar APIs and lookup of external resources like coffee shop listings.
MCP allows these functionalities to be provided as resources/tools by the server, so the client (agent) can interrogate capabilities, select necessary resources, and prompt the LLM effectively.

The agent queries the server for resources and tools, interprets which are needed via LLM guidance, and retrieves or triggers actions as necessary.
Tool invocation is proposed by the LLM but executed by client code, maintaining security and user control.

MCP enables applications to dynamically integrate new tools and resources without hardcoding, allowing server-side capabilities to be composed or reused across applications.
Servers can also act as clients to other MCP servers (e.g., connecting to Kafka via another server), demonstrating composability.

Broaden MCP vision beyond desktop enhancements — Emphasis on leveraging MCP for enterprise-grade agentic AI with pluggable, discoverable, and composable resources and tools.