In the fast-paced world of IT, professionals are constantly seeking ways to optimize processes, enhance collaboration, and drive organizational growth. Bert Blevins is a shining example of how dedication to continuous learning and professional development can lead to transformative impacts in the field of information technology.

Bert Blevins

Security and AI SME

Piter Bowman
Creative Director
Group 45
Group 46

He served as a director of Rotary International Las Vegas and the Las Vegas Chapter of the American Heart Association, and as president of the Houston SharePoint User Group. These roles highlight his dedication to fostering community growth and collaboration.

As a regular speaker at information architecture conferences, Bert has carved out a niche in the IT world, focusing on collaboration, information security, and private blockchain projects. His ability to communicate complex concepts in an accessible manner has made him a sought-after consultant and speaker.

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Bert is a voracious learner, always working to expand his knowledge base to keep up with the rapidly changing world of technology. This dedication to learning ensures that he remains at the forefront of technological advancements, continuously finding new ways to improve and innovate.

In his spare time, Bert indulges in his interest in drones and virtual reality applications. He also challenges himself physically by participating in endurance competitions such as Ironman Triathlons and marathons. This balance of mental and physical pursuits exemplifies his philosophy of making every second count.

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The Model Context Protocol (MCP) architecture is a standardized framework designed to streamline AI interactions with data sources. The MCP Client, which integrates with various AI hosts (e.g., Claude, ChatGPT, Cursor), translates AI requests into a unified protocol format and sends them via MCP to the MCP Server. The MCP Server, acting as an adapter for data sources, processes these requests and fetches or executes operations on external data sources such as files, databases, and APIs, subsequently returning the data back to the client. The MCP flow highlights a seamless communication process where the MCP Client serves as the intermediary, enabling efficient data retrieval and interaction. By standardizing the protocol, MCP ensures compatibility and consistency across different AI systems and data environments, making it a versatile solution for managing complex data workflows.

The Model Context Protocol (MCP) architecture is a system designed to facilitate communication between MCP Hosts (such as Claude Desktop, IDE, and AI Tools) and various data sources including local filesystems, databases, and the internet. MCP Clients act as intermediaries, sending requests to MCP Servers, which process and fetch data from these sources, ensuring seamless interaction. The key components section highlights the MCP Client and Server, supported by transport layers, and includes features like notifications, sampling, tools, resources, and prompts.

The overview presents a step-by-step guide to building a custom MCP (Model Context Protocol) Server using Python, detailing the MCP architecture where MCP Clients interact with an MCP Server via the MCP Protocol to access resources like databases, services, and files. It outlines the process starting with setting up the development environment using MCP Python SDK, FastMCP, AsyncIO, and Requests, followed by creating a basic server structure and developing.

The MCP (Model Context Protocol) workflow showcases how various AI models and agents, including OpenAI, Claude, Deepseek, CrewAI, LangChain, LangGraph, CopilotKit, and Llamaindex, interact with the MCP to access a range of tools and data sources. These tools and data sources encompass GitHub, SingleStore, Slack, Zendesk, Snowflake, Drive, and Dropbox, enabling seamless integration and data processing.

The Model Context Protocol (MCP) architecture overview illustrates how MCP Clients within Agent A and Agent B (MCP HOSTs) communicate with multiple MCP Servers (A, B, C, Y, Z) using the MCP Protocol. It highlights secure collaboration, task and state management, UX negotiation, and capability discovery facilitated by the A2A Protocol between agents. The MCP Servers connect to various data sources, including Local Data Sources 1 and 2, and Internet Web APIs, enabling data access and interaction.