This is the primary of 5 components on this sequence.
1. ELI5: Understanding MCP
Imagine you have got a single common plug that matches all of your units—that’s basically what the Model Context Protocol (MCP) is for AI. MCP is an open commonplace (suppose “USB-C for AI integrations”) that enables AI fashions to hook up with many alternative apps and knowledge sources in a constant approach. In easy phrases, MCP lets an AI assistant discuss to numerous software program instruments utilizing a standard language, as an alternative of every software requiring a special adapter or customized code.
So, what does this imply in apply? If you’re utilizing an AI coding assistant like Cursor or Windsurf, MCP is the shared protocol that lets that assistant use exterior instruments in your behalf. For instance, with MCP an AI mannequin may fetch data from a database, edit a design in Figma, or management a music app—all by sending natural-language directions by a standardized interface. You (or the AI) now not must manually swap contexts or study every software’s API; the MCP “translator” bridges the hole between human language and software program instructions.
In a nutshell, MCP is like giving your AI assistant a common distant management to function all of your digital units and companies. Instead of being caught in its personal world, your AI can now attain out and press the buttons of different purposes safely and intelligently. This widespread protocol means one AI can combine with hundreds of instruments so long as these instruments have an MCP interface—eliminating the necessity for customized integrations for every new app. The consequence: Your AI helper turns into way more succesful, in a position to not simply chat about issues however take actions in the actual software program you utilize.
🧩 Built an MCP that lets Claude discuss on to Blender. It helps you create lovely 3D scenes utilizing simply prompts!
Here’s a demo of me making a “low-poly dragon guarding treasure” scene in only a few sentences👇
Video: Siddharth Ahuja
2. Historical Context: From Text Prediction to Tool-Augmented Agents
To recognize MCP, it helps to recall how AI assistants advanced. Early giant language fashions (LLMs) have been basically intelligent textual content predictors: Given some enter, they’d generate a continuation primarily based on patterns in coaching knowledge. They have been highly effective for answering questions or writing textual content however functionally remoted—they’d no built-in approach to make use of exterior instruments or real-time knowledge. If you requested a 2020-era mannequin to test your calendar or fetch a file, it couldn’t; it solely knew methods to produce textual content.
2023 was a turning level. AI methods like ChatGPT started to combine “tools” and plug-ins. OpenAI launched perform calling and plug-ins, permitting fashions to execute code, use net looking, or name APIs. Other frameworks (LangChain, AutoGPT, and so on.) emerged, enabling multistep “agent” behaviors. These approaches let an LLM act extra like an agent that may plan actions: e.g., search the online, run some code, then reply. However, in these early phases every integration was one-off and advert hoc. Developers needed to wire up every software individually, usually utilizing completely different strategies: One software would possibly require the AI to output JSON; one other wanted a customized Python wrapper; one other a particular immediate format. There was no commonplace approach for an AI to know what instruments can be found or methods to invoke them—it was all hard-coded.
By late 2023, the neighborhood realized that to completely unlock AI brokers, we wanted to maneuver past treating LLMs as solitary oracles. This gave rise to the concept of tool-augmented brokers—AI methods that may observe, plan, and act on the world by way of software program instruments. Developer-focused AI assistants (Cursor, Cline, Windsurf, and so on.) started embedding these brokers into IDEs and workflows, letting the AI learn code, name compilers, run assessments, and so on., along with chatting. Each software integration was immensely highly effective however painfully fragmented: One agent would possibly management an internet browser by producing a Playwright script, whereas one other would possibly management Git by executing shell instructions. There was no unified “language” for these interactions, which made it onerous so as to add new instruments or swap AI fashions.
This is the backdrop in opposition to which Anthropic (the creators of the Claude AI assistant) launched MCP in late 2024. They acknowledged that as LLMs turned extra succesful, the bottleneck was now not the mannequin’s intelligence however its connectivity. Every new knowledge supply or app required bespoke glue code, slowing down innovation. MCP emerged from the necessity to standardize the interface between AI and the large world of software program—very similar to establishing a standard protocol (HTTP) enabled the online’s explosion. It represents the pure subsequent step in LLM evolution: from pure textual content prediction to brokers with instruments (every one customized) to brokers with a common software interface.
3. The Problem MCP Solves
Without MCP, integrating an AI assistant with exterior instruments is a bit like having a bunch of home equipment every with a special plug and no common outlet. Developers have been coping with fragmented integrations in all places. For instance, your AI IDE would possibly use one technique to get code from GitHub, one other to fetch knowledge from a database, and one more to automate a design software—every integration needing a customized adapter. Not solely is that this labor-intensive; it’s brittle and doesn’t scale. As Anthropic put it:
Even essentially the most subtle fashions are constrained by their isolation from knowledge—trapped behind data silos.…Every new knowledge supply requires its personal customized implementation, making actually linked methods tough to scale.
MCP addresses this fragmentation head-on by providing one widespread protocol for all these interactions. Instead of writing separate code for every software, a developer can implement the MCP specification and immediately make their utility accessible to any AI that speaks MCP. This dramatically simplifies the combination matrix: AI platforms must assist solely MCP (not dozens of APIs), and power builders can expose performance as soon as (by way of an MCP server) reasonably than partnering with each AI vendor individually.
Another massive problem was tool-to-tool “language mismatch.” Each software program or service has its personal API, knowledge format, and vocabulary. An AI agent attempting to make use of them needed to know all these nuances. For occasion, telling an AI to fetch a Salesforce report versus querying a SQL database versus modifying a Photoshop file are utterly completely different procedures in a pre-MCP world. This mismatch meant the AI’s “intent” needed to be translated into each software’s distinctive dialect—usually by fragile immediate engineering or customized code. MCP solves this by imposing a structured, self-describing interface: Tools can declare their capabilities in a standardized approach, and the AI can invoke these capabilities by natural-language intents that the MCP server parses. In impact, MCP teaches all instruments a little bit of the identical language, so the AI doesn’t want a thousand phrasebooks.
The result’s a way more strong and scalable structure. Instead of constructing N×M integrations (N instruments occasions M AI fashions), now we have one protocol to rule all of them. As Anthropic’s announcement described, MCP “replaces fragmented integrations with a single protocol,” yielding a easier, extra dependable approach to offer AI entry to the information and actions it wants. This uniformity additionally paves the best way for sustaining context throughout instruments—an AI can carry data from one MCP-enabled software to a different as a result of the interactions share a standard framing. In brief, MCP tackles the combination nightmare by introducing a standard connective tissue, enabling AI brokers to plug into new instruments as simply as a laptop computer accepts a USB gadget.