MCP: What It Is and Why It Matters—Part 1

This is the archetypal of 5 parts successful this series.

1. ELI5: Understanding MCP

Imagine you person a azygous cosmopolitan plug that fits each your devices—that’s fundamentally 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 link to galore antithetic apps and information sources successful a accordant way. In elemental terms, MCP lets an AI adjunct speech to assorted bundle tools utilizing a communal language, alternatively of each instrumentality requiring a antithetic adapter oregon customized code.

So, what does this mean successful practice? If you’re utilizing an AI coding adjunct similar Cursor oregon Windsurf, MCP is the shared protocol that lets that adjunct usage outer tools connected your behalf. For example, with MCP an AI exemplary could fetch accusation from a database, edit a plan successful Figma, oregon power a euphony app—all by sending natural-language instructions done a standardized interface. You (or the AI) nary longer request to manually power contexts oregon larn each tool’s API; the MCP “translator” bridges the spread betwixt quality connection and bundle commands.

In a nutshell, MCP is similar giving your AI adjunct a universal distant control to run each your integer devices and services. Instead of being stuck successful its ain world, your AI tin present scope retired and property the buttons of different applications safely and intelligently. This communal protocol means one AI tin integrate with thousands of tools arsenic agelong arsenic those tools person an MCP interface—eliminating the request for customized integrations for each caller app. The result: Your AI helper becomes acold much capable, capable to not conscionable chat astir things but take actions successful the existent bundle you use.

🧩 Built an MCP that lets Claude speech straight to Blender. It helps you make beauteous 3D scenes utilizing conscionable prompts!

Here’s a demo of maine creating a “low-poly dragon guarding treasure” country successful conscionable a fewer sentences👇

Video: Siddharth Ahuja

2. Historical Context: From Text Prediction to Tool-Augmented Agents

To admit MCP, it helps to callback however AI assistants evolved. Early ample connection models (LLMs) were fundamentally clever text predictors: Given immoderate input, they’d make a continuation based connected patterns successful grooming data. They were almighty for answering questions oregon penning substance but functionally isolated—they had no built-in mode to usage outer tools oregon real-time data. If you asked a 2020-era exemplary to cheque your calendar oregon fetch a file, it couldn’t; it lone knew however to nutrient text.

2023 was a turning point. AI systems similar ChatGPT began to integrate “tools” and plug-ins. OpenAI introduced relation calling and plug-ins, allowing models to execute code, usage web browsing, oregon telephone APIs. Other frameworks (LangChain, AutoGPT, etc.) emerged, enabling multistep “agent” behaviors. These approaches fto an LLM enactment much similar an agent that tin program actions: e.g., hunt the web, tally immoderate code, past answer. However, successful these aboriginal stages each integration was one-off and advertisement hoc. Developers had to ligament up each instrumentality separately, often utilizing antithetic methods: One instrumentality mightiness necessitate the AI to output JSON; different needed a customized Python wrapper; different a peculiar punctual format. There was no modular way for an AI to cognize what tools are disposable oregon however to invoke them—it was each hard-coded.

By late 2023, the assemblage realized that to afloat unlock AI agents, we needed to determination beyond treating LLMs arsenic solitary oracles. This gave emergence to the thought of tool-augmented agents—AI systems that tin observe, plan, and act on the satellite via bundle tools. Developer-focused AI assistants (Cursor, Cline, Windsurf, etc.) began embedding these agents into IDEs and workflows, letting the AI work code, telephone compilers, tally tests, etc., successful summation to chatting. Each instrumentality integration was immensely almighty but painfully fragmented: One cause mightiness power a web browser by generating a Playwright script, portion different mightiness power Git by executing ammunition commands. There was nary unified “language” for these interactions, which made it hard to adhd caller tools oregon power AI models.

This is the backdrop against which Anthropic (the creators of the Claude AI assistant) introduced MCP successful late 2024. They recognized that arsenic LLMs became much capable, the bottleneck was nary longer the model’s quality but its connectivity. Every caller information root oregon app required bespoke glue code, slowing down innovation. MCP emerged from the request to standardize the interface betwixt AI and the wide satellite of software—much similar establishing a communal protocol (HTTP) enabled the web’s explosion. It represents the earthy adjacent measurement successful LLM evolution: from axenic substance prediction to agents with tools (each 1 custom) to agents with a cosmopolitan instrumentality interface.

3. The Problem MCP Solves

Without MCP, integrating an AI adjunct with outer tools is simply a spot similar having a clump of appliances each with a antithetic plug and nary cosmopolitan outlet. Developers were dealing with fragmented integrations everywhere. For example, your AI IDE mightiness usage 1 method to get codification from GitHub, different to fetch information from a database, and yet different to automate a plan tool—each integration needing a customized adapter. Not lone is this labor-intensive; it’s brittle and doesn’t scale. As Anthropic enactment it:

Even the astir blase models are constrained by their isolation from data—trapped down accusation silos.…Every caller information root requires its ain customized implementation, making genuinely connected systems hard to scale.

MCP addresses this fragmentation head-on by offering one communal protocol for each these interactions. Instead of penning abstracted codification for each tool, a developer tin instrumentality the MCP specification and instantly marque their exertion accessible to immoderate AI that speaks MCP. This dramatically simplifies the integration matrix: AI platforms request to enactment lone MCP (not dozens of APIs), and instrumentality developers tin exposure functionality erstwhile (via an MCP server) alternatively than partnering with each AI vendor separately.

Another large situation was tool-to-tool “language mismatch.” Each bundle oregon work has its ain API, information format, and vocabulary. An AI cause trying to usage them had to cognize each these nuances. For instance, telling an AI to fetch a Salesforce study versus querying a SQL database versus editing a Photoshop record are wholly antithetic procedures successful a pre-MCP world. This mismatch meant the AI’s “intent” had to beryllium translated into each tool’s unsocial dialect—often by fragile punctual engineering oregon customized code. MCP solves this by imposing a structured, self-describing interface: Tools tin declare their capabilities successful a standardized way, and the AI tin invoke those capabilities done natural-language intents that the MCP server parses. In effect, MCP teaches each tools a spot of the same language, truthful the AI doesn’t request a 1000 phrasebooks.

The effect is simply a overmuch much robust and scalable architecture. Instead of gathering N×M integrations (N tools times M AI models), we person one protocol to regularisation them all. As Anthropic’s announcement described, MCP “replaces fragmented integrations with a azygous protocol,” yielding a simpler, much reliable way to springiness AI entree to the information and actions it needs. This uniformity besides paves the mode for maintaining discourse crossed tools—an AI tin transportation cognition from 1 MCP-enabled instrumentality to different due to the fact that the interactions stock a communal framing. In short, MCP tackles the integration nightmare by introducing a communal connective tissue, enabling AI agents to plug into caller tools as easy arsenic a laptop accepts a USB device.