Unpacking MCP: The 'USB-C Port' for AI Changing the Game for Claude
Z
Zack Saadioui
8/10/2025
Unpacking MCP: The "USB-C Port" for AI that's Changing the Game for Claude
Hey everyone, let's talk about something that’s quietly revolutionizing how we build with AI, especially if you’re a fan of Anthropic’s Claude. It's called the Model Context Protocol, or MCP, & honestly, it's one of the most exciting developments I've seen in a while.
If you’ve ever tried to get an AI model to do something truly useful, you know the struggle. You want it to read a file, check a database, or ping an API. Each of these tasks requires custom code, specific integrations, & a whole lot of duct tape to hold it all together. It's brittle, time-consuming, & just plain messy.
Well, that's the world MCP is here to fix. Think of it like this: remember the chaos of having a different charger for every single device? A drawer full of tangled cables for your phone, your camera, your portable speaker... it was a nightmare. Then USB-C came along & started to standardize everything. One port, one cable, for almost everything.
That's EXACTLY what MCP is for AI. It’s an open-source standard that creates a universal way for AI models, like Claude, to connect to all sorts of external tools, data sources, & APIs. No more building one-off integrations for every little thing. It’s a standardized plug-and-play system for AI context, & it’s a total game-changer.
So, What's the Big Deal? The Core Idea Behind MCP
At its heart, MCP is all about creating a common language. It’s a protocol, a set of rules for communication, that allows an AI application (the "host") to talk to various tools (the "servers"). This simple idea has some pretty profound implications.
Before MCP, if you wanted your AI chatbot to, say, pull user data from your CRM, you’d have to write specific code that knew how to authenticate with that CRM, how to format the request, & how to parse the response. Then, if you wanted it to also check inventory in your Shopify store, you'd have to start all over again, writing completely different code for the Shopify API.
This is where businesses often get stuck. They have powerful AI models, but getting those models to interact with their unique, internal systems is a massive hurdle. This is where a solution like Arsturn can be incredibly powerful. Imagine you've built a custom AI chatbot with Arsturn, trained on all your company's product documentation & support articles. Your customers can get instant answers 24/7. But what if a customer asks, "What's the status of my order?" That information isn't in your documentation; it's in your order management system.
With an MCP-like philosophy, your Arsturn bot could seamlessly connect to that system. The bot understands the intent of the question, uses a standardized protocol to ask the order management "server" for the information, & then gives the customer a real-time update. This turns a simple Q&A bot into a dynamic, interactive assistant that can actually do things for your customers.
MCP formalizes this connection. It defines how the AI application can discover what tools are available, understand what they do, & execute them securely.
The Moving Parts: Understanding the MCP Architecture
To really get it, you need to know the three main players in the MCP ecosystem. It's a classic client-server model, but with a slight twist.
The MCP Host: This is your main AI application. Think of apps like Claude Code or Claude Desktop. The host is the brain of the operation; it's what the user interacts with. Its job is to manage all the connections to the various tools you want to use.
The MCP Client: For every tool or data source you want to connect to, the host creates a dedicated MCP client. So if you have a connection to your filesystem & another one to a GitHub repository, the host will spin up two separate clients. Each client is responsible for maintaining that one-to-one connection with its server.
The MCP Server: This is the program that actually provides the tools & context. A server might wrap your company's internal database, expose a set of API endpoints, or even provide access to your local files. The cool thing is, these servers can be anywhere. A server could be running locally on your machine (like one that lets Claude read your project files) or it could be a remote server hosted by another company (like a Sentry server for error tracking).
The flow is pretty straightforward: The MCP Host (e.g., Claude Code) connects to an MCP Server (e.g., a server for your company's wiki). It does this by creating an MCP Client. Once connected, the server tells the client about all the tools it has to offer. Maybe it has a
1
search_wiki(query)
tool & a
1
get_page(title)
tool.
Now, when you're chatting with Claude & you ask, "Hey, can you look up the company's policy on remote work?", Claude sees that the wiki server has a
1
search_wiki
tool that looks relevant. It can then decide to use that tool, passing your query along. The server runs the search, returns the results, & Claude presents them to you in a natural, conversational way.
The Building Blocks: Tools, Resources, & Prompts
MCP isn't just about connecting things; it's about defining what can be shared. The protocol breaks this down into three core "primitives," which are just fancy words for the types of things a server can offer.
Tools: These are the action-takers. They are executable functions that the AI can call to do something. Think
1
send_email
,
1
query_database
,
1
create_jira_ticket
, or
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edit_file
. This is where the magic really happens, as it turns a passive model into an active agent that can perform tasks in the real world. Claude Code, for instance, has its own built-in tools like
1
View
,
1
Edit
, &
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LS
(for listing files) that it exposes via an MCP server.
Resources: These are data sources. They provide the contextual information the AI needs to be smart. This could be the content of a file, a record from a database, or the response from an API call. For example, a server could provide a "resource" representing your entire codebase, allowing the AI to understand the context of your project without you having to copy-paste code into the prompt.
Prompts: These are reusable templates for interacting with the language model. They can help structure conversations or provide a set of instructions for how the AI should behave. This is super useful for ensuring consistency & steering the model's behavior in complex workflows.
By standardizing these three primitives, MCP creates a rich & predictable way for AI applications to get the context they need to be truly helpful assistants.
Why This is a HUGE Deal for Claude Code & Developers
Okay, so we've got the theory. But why does this matter? Especially for anyone using Claude Code?
The answer is simple: leverage & efficiency.
Claude Code is already a powerful coding assistant. But by acting as an MCP host, it transforms from a smart code editor into an integrated development environment that's deeply aware of your entire workflow.
Imagine you're debugging an issue. You can ask Claude to:
"Read the latest error logs from Sentry." Claude connects to a Sentry MCP server to fetch the logs.
"Okay, it looks like the error is in the
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user_auth.py
file. Can you show me that file?" Claude uses a local filesystem MCP server to read the file & display it.
"Now, cross-reference that with the related ticket in Jira." It connects to a Jira MCP server to pull up the ticket details.
"Alright, I think I know the fix. Apply this change to the file & then run the tests." Claude uses the filesystem server to edit the file & another server to trigger your CI/CD pipeline.
This entire workflow happens in a single, conversational interface. You're not switching between five different windows, copying & pasting information. You're just... talking to your development environment. This is the future of software development, & MCP is the backbone that makes it possible.
For businesses, this opens up a whole new world of automation. Think about your customer support team. They're constantly jumping between a CRM, a knowledge base, a payment processor, & a ticketing system. It's inefficient & drains cognitive load.
Now, imagine equipping them with an internal tool powered by this same principle. An Arsturn-powered internal chatbot, for instance, could act as the MCP host. When a support agent gets a query, they can just ask the bot: "Pull up the latest ticket from Jane Doe, check her payment history in Stripe, & summarize her issue from the past three support chats."
The bot, using MCP connectors, seamlessly fetches information from all those different systems & presents a single, unified summary to the agent. This is not just a chatbot; it's a productivity multiplier. Arsturn helps businesses build these kinds of no-code AI chatbots, trained on their own data, to boost conversions & provide these incredibly personalized & efficient customer (or employee) experiences. By integrating with internal systems through a standardized protocol, the chatbot becomes exponentially more valuable.
Getting Practical: How MCP Works in the Real World
This isn't just a theoretical standard; you can start using it right now. The ecosystem is still growing, but the foundations are solid.
Installation & Configuration: MCP servers can be set up in different "scopes." You can install a server that's only available for a specific project (
1
project
scope), one that's available for all your projects (
1
user
scope), or one tied to a specific local setup (
1
local
scope). This is managed through a simple JSON configuration file, where you tell your MCP host (like Claude Desktop) where to find & how to run the servers you want to use.
For example, to add a server that lets you search the web using Brave Search, you might add a snippet to your config file that tells Claude how to launch the Brave Search MCP server.
Authentication: Of course, you don't want your AI to have free rein over all your private data. MCP has security baked in. Many remote servers require authentication, & the protocol supports standards like OAuth 2.0. When you first try to use a tool that needs it, Claude will guide you through a login process in your browser. The authentication tokens are then stored securely & refreshed automatically, so you only have to do it once.
Building Your Own Server: Here's where it gets REALLY exciting. Because MCP is an open protocol, anyone can build a server for it. Anthropic and the community provide SDKs for different programming languages that handle a lot of the boilerplate code for you.
Got a proprietary internal database? You can build a simple MCP server that exposes a few key functions for querying it. Want to connect Claude to your smart home devices? You could build an MCP server that wraps their APIs. The possibilities are genuinely endless. This transforms Claude from a tool that just uses pre-approved integrations into a platform that you can extend to fit your exact needs.
The Bigger Picture: MCP & the Future of AI Agents
MCP isn't just about making Claude better. It's a foundational piece of a much larger puzzle: the rise of capable AI agents.
For years, we've been promised AI assistants that can understand our goals & take complex actions on our behalf. The problem has always been the connection to the real world. An LLM is just a brain in a jar; it needs hands & eyes to do anything.
Function calling was a step in the right direction, but it still required developers to manually define the functions available for each interaction. MCP is the next evolution. It allows an AI to dynamically discover & utilize a whole ecosystem of tools, just like a person might learn to use different software applications.
This creates a virtuous cycle:
More people build useful MCP servers for their tools & APIs.
AI hosts like Claude become more powerful because they have access to more tools.
This increased capability encourages more users & developers to adopt the platform.
This larger user base incentivizes even more companies to build MCP servers.
This is how you build a powerful, open ecosystem, rather than a closed, walled garden where the platform owner dictates all the integrations.
Wrapping it Up
So, the Model Context Protocol. It sounds technical, & in the weeds, it is. But the concept is simple & powerful. It’s the USB-C port for AI, a universal standard that lets models like Claude plug into the world.
For developers, it means building more powerful, context-aware applications with less effort. It means turning your development environment into a true conversational partner.
For businesses, it’s a pathway to meaningful automation. It's about connecting your AI investments—like the custom chatbots you can build with Arsturn—to the real-world data & systems that run your company. It’s about moving from simple AI chat to interactive AI agents that can generate leads, solve customer problems, & make your teams more productive.
Honestly, it's a huge leap forward. We're just scratching the surface of what's possible when you give AI a standardized way to connect to everything else. It’s going to be pretty cool to see what people build with it.
Hope this was helpful & gave you a good sense of what all the buzz is about. Let me know what you think