Max Headroom 
This is the first of four parts in this series.
1. ELI5: Understanding MCP
Imagine you have a single universal plug that fits all your devices—that’s essentially what the Model Context Protocol (MCP) is for AI. MCP is an open standard (think “USB-C for AI integrations”) that allows AI models to connect to many different apps and data sources in a consistent way. In simple terms, MCP lets an AI assistant talk to various software tools using a common language, instead of each tool requiring a different adapter or custom code.
So, what does this mean in practice? If you’re using an AI coding assistant like Cursor or Windsurf, MCP is the shared protocol that lets that assistant use external tools on your behalf. For example, with MCP an AI model could fetch information from a database, edit a design in Figma, or control a music app—all by sending natural-language instructions through a standardized interface. You (or the AI) no longer need to manually switch contexts or learn each tool’s API; the MCP “translator” bridges the gap between human language and software commands.
In a nutshell, MCP is like giving your AI assistant a universal remote control to operate all your digital devices and services. Instead of being stuck in its own world, your AI can now reach out and press the buttons of other applications safely and intelligently. This common protocol means one AI can integrate with thousands of tools as long as those tools have an MCP interface—eliminating the need for custom integrations for each new app. The result: Your AI helper becomes far more capable, able to not just chat about things but take actions in the real software you use.
Built an MCP that lets Claude talk directly to Blender. It helps you create beautiful 3D scenes using just prompts!
Here’s a demo of me creating a “low-poly dragon guarding treasure” scene in just a few sentences
Video: Siddharth Ahuja
2. Historical Context: From Text Prediction to Tool-Augmented Agents
To appreciate MCP, it helps to recall how AI assistants evolved. Early large language models (LLMs) were essentially clever text predictors: Given some input, they’d generate a continuation based on patterns in training data. They were powerful for answering questions or writing text but functionally isolated—they had no built-in way to use external tools or real-time data. If you asked a 2020-era model to check your calendar or fetch a file, it couldn’t; it only knew how to produce text.
2023 was a turning point. AI systems like ChatGPT began to integrate “tools” and plug-ins. OpenAI introduced function calling and plug-ins, allowing models to execute code, use web browsing, or call APIs. Other frameworks (LangChain, AutoGPT, etc.) emerged, enabling multistep “agent” behaviors. These approaches let an LLM act more like an agent that can plan actions: e.g., search the web, run some code, then answer. However, in these early stages each integration was one-off and ad hoc. Developers had to wire up each tool separately, often using different methods: One tool might require the AI to output JSON; another needed a custom Python wrapper; another a special prompt format. There was no standard way for an AI to know what tools are available or how to invoke them—it was all hard-coded.
By late 2023, the community realized that to fully unlock AI agents, we needed to move beyond treating LLMs as solitary oracles. This gave rise to the idea of tool-augmented agents—AI systems that can observe, plan, and act on the world via software tools. Developer-focused AI assistants (Cursor, Cline, Windsurf, etc.) began embedding these agents into IDEs and workflows, letting the AI read code, call compilers, run tests, etc., in addition to chatting. Each tool integration was immensely powerful but painfully fragmented: One agent might control a web browser by generating a Playwright script, while another might control Git by executing shell commands. There was no unified “language” for these interactions, which made it hard to add new tools or switch AI models.
This is the backdrop against which Anthropic (the creators of the Claude AI assistant) introduced MCP in late 2024. They recognized that as LLMs became more capable, the bottleneck was no longer the model’s intelligence but its connectivity. Every new data source or app required bespoke glue code, slowing down innovation. MCP emerged from the need to standardize the interface between AI and the wide world of software—much like establishing a common protocol (HTTP) enabled the web’s explosion. It represents the natural next step in LLM evolution: from pure text prediction to agents with tools (each one custom) to agents with a universal tool interface.
3. The Problem MCP Solves
Without MCP, integrating an AI assistant with external tools is a bit like having a bunch of appliances each with a different plug and no universal outlet. Developers were dealing with fragmented integrations everywhere. For example, your AI IDE might use one method to get code from GitHub, another to fetch data from a database, and yet another to automate a design tool—each integration needing a custom adapter. Not only is this labor-intensive; it’s brittle and doesn’t scale. As Anthropic put it:
Even the most sophisticated models are constrained by their isolation from data—trapped behind information silos.…Every new data source requires its own custom implementation, making truly connected systems difficult to scale.
MCP addresses this fragmentation head-on by offering one common protocol for all these interactions. Instead of writing separate code for each tool, a developer can implement the MCP specification and instantly make their application accessible to any AI that speaks MCP. This dramatically simplifies the integration matrix: AI platforms need to support only MCP (not dozens of APIs), and tool developers can expose functionality once (via an MCP server) rather than partnering with every AI vendor separately.
Another big challenge was tool-to-tool “language mismatch.” Each software or service has its own API, data format, and vocabulary. An AI agent trying to use them had to know all these nuances. For instance, telling an AI to fetch a Salesforce report versus querying a SQL database versus editing a Photoshop file are completely different procedures in a pre-MCP world. This mismatch meant the AI’s “intent” had to be translated into every tool’s unique dialect—often by fragile prompt engineering or custom code. MCP solves this by imposing a structured, self-describing interface: Tools can declare their capabilities in a standardized way, and the AI can invoke those capabilities through natural-language intents that the MCP server parses. In effect, MCP teaches all tools a bit of the same language, so the AI doesn’t need a thousand phrasebooks.
The result is a much more robust and scalable architecture. Instead of building N×M integrations (N tools times M AI models), we have one protocol to rule them all. As Anthropic’s announcement described, MCP “replaces fragmented integrations with a single protocol,” yielding a simpler, more reliable way to give AI access to the data and actions it needs. This uniformity also paves the way for maintaining context across tools—an AI can carry knowledge from one MCP-enabled tool to another because the interactions share a common framing. In short, MCP tackles the integration nightmare by introducing a common connective tissue, enabling AI agents to plug into new tools as easily as a laptop accepts a USB device.
AI & ML, Artificial Intelligence, Deep Dive
Radar
