AI Agents Using MCP = Your “Guy in the Chair”
Tutorials, examples, code, and guidelines to give your AI Agents great power (and great responsibility).
In 1982, at the heart of the Tron universe was the MCP, or Master Control Program. It was an old-fashioned mental-model of a computing system.
It represented an “all roads lead to Rome” mindset where all code stemmed from a single core. This was the era of linear coding: programs that ran line-by-line. No weird async insanity. No ghost-like classes and instantiations. No polymorphism. No “listeners” waiting to spring into action. No non-deterministic chaos. Certainly no cosine similarity traversing through n-dimensional latent space. The mental model of the time was that a program was a recipe or a robot that did what you told it to do (and only what you told it to do). It was a clean mindset. Deterministic. Logical. If there was a bug, it was findable (eventually) somewhere between your fingers and the metal. And even back then this was more or less Hollywood reductionism rather than how people actually coded. Coders have always found artistic, poetic, elegant ways to emulate nature’s raw glory with ingenious complications and redirections and mis-appropriations all in the service of finding a way.
Today, MCP now stands for something totally different: Model Context Protocol (MCP) and it’s a way for AI Agents to talk to other tools. The P in MCP stands for protocol. And just like there’s no actual magical power infused in the letters of the alphabet, there is also nothing special about a protocol, and yet, its mere existence means quite interesting actions can now happen. With a codified alphabet we can communicate any human thought that can be put into words, and with a codified MCP, AI Agents can now start to use the Internet, tools, and anything else we can do with our keyboards and cursor.
You can use the protocol to help your AI Agents accomplish complex and branching workflows: so that the Agent can decide which tools to use, in what order to use them, how to chain them together, what to do with the results you get back, and when to ask for an opinion or permission from a human in the loop. So unlike Tron, were all roads pointed to the central core, the agentic version of MCP is more like Ned, the “guy in the chair” in Spider-man movies… where a helpful friend is plugging into whatever systems it needs to get the information you need to get the job done.
Agents using MCP = Your Guy in the Chair
Where did this all come from?
The mindset that brought us to the agentic MCP has its origins in how devs like to code: drop into your coding environment (VSCode, Cursor, Warp, etc) and just stay there…lock in… headphones on… no need to jump out to your database or open up some other tool. Stay locked into the flow of code and send runners out to interact with the other tools and systems. Don’t break your flow state by opening up some other tool or program and have to transition to some other context… eliminated all “context-switching costs” and stay locked in and just fetch what you need.
History of MCP
The Model Context Protocol (MCP) was “developed by Anthropic to create a standard for how language models interact with external tools and services.” (so says Claude). MCP has since spread to OpenAI and Google’s Gemini.
TL;DR
- The Model Context Protocol (MCP) allows AI models to interact with external tools, fetch data, and interact with services.
- MCP is designed to support autonomous AI workflows, allowing AI agents to decide which tools to use, in what order, and how to chain them together to accomplish tasks.
- MCP also includes a human-in-the-loop feature for humans to provide additional data and approve execution.
- AI Agents Using MCP = Your Guy in the Chair
AI MCP Use Cases:
Examples & Why They Matter
I asked Claude and OpenAI to collaborate on a list of resources. Here are the results.
🎨 Creative Tools
Figma MCP Server
- Link: GitHub Repository
- Why this matters: Enables AI agents to interact directly with Figma files, automating UI/UX design workflows and bridging the gap between design and development.
AI-Controlled Ableton Live Project
- Link: YouTube Demo
- Why this matters: Demonstrates real-time AI interaction in music workflows, paving the way for novel AI-assisted creative processes and compositions.
Blender MCP Integration
- Link: Blender MCP
- Why this matters: Allows intuitive, text-driven creation and modification of 3D models, drastically simplifying traditional 3D modeling processes.
🧠 AI-Orchestrated Multi-Tool Workflows
AI Control Center Concept
- Link: Cline.bot Article
- Why this matters: Highlights AI’s potential to orchestrate complex, multi-application tasks, significantly enhancing productivity and creative possibilities.
MCP-First Development Approach
- Link: Medium Article
- Why this matters: Promotes AI-ready app development, enabling seamless AI-agent integration across multiple tools from inception.
🧰 AI-Enhanced Developer Tools
Cursor IDE with MCP Support
- Link: Dev.to Article
- Why this matters: Embeds AI directly into coding environments, providing immediate access to tools like GitHub and databases through natural language.
Spring AI MCP SDK
- Link: GitHub Repository
- Why this matters: Simplifies the integration of AI functionality into Java applications, enabling standardized development of powerful AI-enhanced applications.
🏥 Healthcare Applications
MediAssist MCP Framework
- Link: Research Paper
- Why this matters: Enables AI assistants to securely access and analyze patient records, medical imaging, and lab results, aiding physicians in making informed decisions.
OpenMedicalAI Research Project
- Link: GitHub Repository
- Why this matters: Open-source initiative allowing medical researchers to build specialized diagnostic tools that coordinate between multiple hospital systems through a standardized MCP interface.
💰 Financial Services
FinMCP Open Banking Framework
- Link: Infoupdate.org Article
- Why this matters: Creates secure connections between AI financial advisors and multiple financial data sources, enabling comprehensive financial analysis through natural language.
AlgoTrader MCP Integration
- Link: Wyden.io Article
- Why this matters: Allows quantitative analysts to design, test, and deploy trading algorithms through conversational interfaces coordinating between market data feeds and execution platforms.
🔬 Scientific Research
OpenLab MCP Server
- Link: Agilent OpenLab Server
- Why this matters: Connects AI assistants directly to lab equipment and research databases, allowing researchers to design experiments and analyze results through natural language commands.
Research Data Orchestration Platform
- Link: Semantic Scholar Paper
- Why this matters: Enables interdisciplinary research teams to unify disparate datasets and analytical tools through a common MCP interface, accelerating discovery in complex scientific domains.
📚 Education
EduMCP Framework
- Link: GitHub Repository
- Why this matters: Connects AI tutors to learning management systems and assessment tools, creating personalized learning experiences that adapt to individual student needs.
Language Learning MCP Demo
- Link: YouTube Tutorial
- Why this matters: Shows how AI language tutors can coordinate between pronunciation analysis tools and grammar checkers to create immersive language learning experiences.
🛒 E-commerce and Retail
ShopOps MCP Suite
- Link: RepairDOC Overview
- Why this matters: Allows AI assistants to manage online stores by coordinating between inventory systems, customer service platforms, and analytics tools, streamlining e-commerce operations.
Smart Retail Analytics Platform
- Link: Dreamstime Illustration
- Why this matters: Connects in-store sensors, customer data, and inventory management systems through a unified MCP interface, enabling AI to provide real-time business intelligence for physical retail environments.
📘 Top Tutorials for Getting Started with MCP
- Building an AI Agent with MCP and LangChain Adapters
- Author: Seenivasa Ramadurai
- Platform: DEV Community
- Link: Read Tutorial
- Overview: Guides you through developing a multi-functional AI agent using Anthropic’s MCP with LangChain MCP adapters.
2. Model Context Protocol (MCP): A Guide With Demo Project
- Author: Aashi Dutt
- Platform: DataCamp
- Link: Read Tutorial
