A Deep-Dive Into Model Context Protocol (MCP)

Large language models (LLMs) are becoming increasingly capable. You can use them to transform business workflows, build agents, create data pipelines, or launch smart agentic AI assistants. 

However, LLMs don’t work in isolation, they need access to data. So, when building any application LLMs, you would be forced to figure out how to integrate it with myriad data sources, and transform it into a format that works with the LLM. 

Enter Anthropic’s Model Context Protocol (MCP). Introduced in November 2024, it aims to standardize these connections, much like USB does for hardware devices. 

At its core, MCP is an open protocol that defines a common language for AI applications to interact seamlessly with external data sources and tools. Numerous organizations, including OpenAI (Anthropic’s competitor) have accepted the standard, so it is here to stay. 

Before MCP, developers had to create custom integrations for each data source, leading to duplicated efforts and maintenance challenges. MCP addresses this by providing a universal standard, allowing AI systems to access various data sources through a single, consistent interface.

At this point, you are probably wondering why can’t I simply use REST APIs, GraphQL queries, SDKs, or handcode the integrations? In this article, I will explain why MCP is a leap forward from traditional methods. I will also demonstrate how to rapidly launch AI systems in your organization using MCP.

Why Do You Need Model Context Protocol (MCP)? 

The best way to understand why MCP is to compare it against, say, building REST APIs to integrate LLMs with data. 

Here’s what you had to do to integrate REST APIs into an AI assistant or app: 

• You read the docs.
• Authenticate.
• Format your requests.
• Parse the responses.
• Deal with rate limits, pagination, weird edge cases.
• Do it all again for each different service (Google Drive, Notion, GitHub, etc.).

Your effort would have grown as the number of data sources would increase. Over time, it becomes a maintenance nightmare. 

Here’s the thing: REST APIs were made for software developers to write code against. They return raw JSON or XML meant for human-crafted apps to process. 

MCP flips the paradigm: it is designed for LLMs to "understand" and reason over the structure of external tools and data. It abstracts away API syntax and offers AI-native context objects.

Since MCP offers a standard protocol, an AI assistant can plug into any tool that supports MCP with no special-case code, just like your browser uses HTTP for every website.

Also, it supports contextual streaming. The AI assistants can receive live, structured context from tools continuously, without polling or repeated API calls.

MCP Architecture

Let’s get under the hood and explore how the MCP actually works. At its heart, MCP follows a client-server architecture, where a host application can connect to multiple servers. 

When you build an AI assistant or agent, your system acts as the MCP client. It doesn’t have to know the details of how Slack works or how GitHub’s API is structured. It just knows how to talk to MCP servers.

You can think of the client as the "brain" (the LLM), and it uses MCP to fetch relevant knowledge from external sources, like reaching into a toolbox.

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MCP Servers

Each external service, say, Notion or Jira or your even your microservice, needs an MCP server to act as a translator.

• The server knows how to speak to the external API (e.g., Jira's REST API).
• It also knows how to format and expose that data in a way that the LLM can understand.
• It translates LLM queries into API calls and returns structured, context-rich responses.

You or your platform vendor can either build your own MCP server or use an existing open-source one. 

Secure, Session-Based Connections

When the client (your assistant) wants to connect to a tool:

1. It initiates a session with an MCP server.
2. The server verifies the identity and permissions of the user (you control what the assistant can access).
3. Once connected, the assistant can:
   
   • Browse available data sources.
   • Ask for specific structured context (e.g., "Show me open PRs assigned to Alice").
   • Even stream updates in real-time.

Sessions are scoped, temporary, and revocable, which means you can trust that your data isn’t being leaked or accessed beyond what’s needed.

Developers Build Servers, Assistants Stay Clean

The beauty of this architecture is separation of concerns:

• If you’re building a tool, you expose it via an MCP server.
• If you’re building an AI agent, you just connect to that server—you don’t need to touch the tool’s API at all.

This keeps the assistant codebase lightweight, flexible, and focused on intelligence—not integration plumbing.

Let’s Recap

So to summarize:

• MCP Clients = LLM-based agents that want structured external context.
• MCP Servers = Gateways that expose tools and data in an LLM-friendly format.
• Sessions = Secure, scoped connections between the two.
• The result? A scalable, pluggable architecture for AI that can reason with real-world tools.

A large number of technology platforms have already adopted MCP. This means that you don’t need to write custom adapters for every tool; just plug into an MCP server and go.

Building an MCP-Powered AI Agent

Let’s go a little deeper, and explore how to build an agent. This is the best way to understand its capabilities. Later, we will discuss its applications and how you can use it for your organization. 

To showcase how the protocol works, we will build an MCP client and and a server. 

So that you understand how to build agents that use your internal data, as well as external sources, we will build a financial portfolio news tracker agent. The agent would: 

• Connect to an MCP server that fetches list of stocks from the database. 
• Connect to a second MCP server that is built to fetch news using stock symbols. 
• Respond back with an answer that allows you to see the news around all your stocks. 

Follow this link to go through the implementation steps. You can also subscribe to Superteams.ai Academy to stay updated with the latest in AI.

List of MCP Servers

Numerous technology platforms have started releasing their MCP servers. Here’s a growing list of MCP servers you can use out of the box or extend for your stack:

Data & Files

• filesystem – Read and navigate local or mounted file systems securely.
• google_drive – Pull and search files directly from Google Drive.
• postgres – Query structured data from Postgres databases (read-only).
• sqlite – Great for quick prototypes using local data.

Dev & Infra

• git – Surface commits, diffs, branches—everything from a Git repo.
• github – Connect to GitHub APIs: repos, issues, pull requests, and more.
• gitlab – Same idea, GitLab flavor—project info, merge requests, etc.
• sentry – Pull production issues and stack traces into your agent’s context.

Communication

• gmail – Search, read, and even draft emails from your Gmail inbox.
• slack – Let your agent join conversations, summarize channels, or watch threads.
• intercom – Grab user conversations, ticket metadata, and support history.

Project Management

• linear – Create, update, and search for tasks/issues in Linear.
• jira – All your enterprise issue tracking, now in structured LLM context.
• asana – Perfect for AI copilots tracking task updates across teams.

Cloud & Infra

• kubernetes – Let your agent monitor and interact with cluster resources.
• aws – Access cloud infra: S3, EC2, RDS—you name it.
• azure – Manage Azure services and get telemetry straight into the model.

Content & Publishing

• ghost – Write, edit, and manage blog posts using Ghost CMS.
• wordpress – Fetch and publish WordPress content programmatically.

You can visit the growing list here: 

• MCP.so
• Awesome MCP Servers
• Portkey

Applications of Model Context Protocol (MCP)

Now that you’ve seen how MCP works under the hood, let’s talk about what you can actually do with it.

This is where things get exciting, because once your AI assistant has structured, real-time context from your tools, the use cases multiply fast. 

Let’s walk through a few powerful applications. 

AI Analysts with Live Data Context

Want an LLM that understands your business metrics without dumping your database into a prompt?

With an MCP server exposing data from PostgreSQL, Hadoop, Spark, Elastic or APIs, your agent can:

• Pull in real-time sales data.
• Compare revenue trends across quarters.
• Explain anomalies in plain English.

This simplifies the process for product managers or finance team, who don’t need to depend on devs to create dashboards or fetch data for them. 

Manufacturing – AI Production Assistant

You can build MCP systems that allows to understand your factory pipeline through simple text queries. It can: 

• Pull real-time machine logs from MES (Manufacturing Execution System) via an MCP server
• Analyze for anomalies and alert plant supervisors
• File maintenance tickets or reorder parts via ERP systems

You can then ask queries like: “Why did Line 4 pause for 23 minutes yesterday?”

The agent will check log context, flag a sensor spike, and give you links to the maintenance ticket.

Customer Support – AI QA & Insights Bot

In this scenario, the system can assist you in streamlining your customer support workflows. It can: 

• Connect to Zendesk / Intercom / CRM tools via MCP
• Analyze sentiment across recent tickets
• Suggest new help articles or surface product bugs

For instance, you can then ask the agent “Are we seeing an uptick in complaints post latest product update?” 

The agent pulls and clusters tickets by topic, flags the spike, and suggests a root cause.

Logistics & Supply Chain – AI Inventory Analyst

You can build agents that simplify supply chain querying. It can:

• Connect warehouse databases, shipment trackers, and order systems via MCP
• Monitor stock levels and reordering schedules
• Automatically escalate when SLA breaches are predicted

You can then query the agent like “Which suppliers are consistently late and affecting stockouts?” The agent would return a ranked list with on-time delivery rates and affected SKUs.

The use-cases are endless. The ability to rapidly pull together various platforms and build intelligent agents will transform how technology is built in the future.

How Superteams.ai Helps Organizations Build MCP-Powered Agents

Wondering how to actually bring it into your org without blowing up your existing systems or distracting your dev team?

That’s where Superteams.ai comes in.

We work like your extended R&D task force, rolling up our sleeves to help you design and build AI agents that are not just technically sound, but actually usable by your teams.

Here’s what we do, step-by-step:

We Design the Right Architecture for Your Stack

We start by understanding your tools, workflows, and what kind of assistant you want to build. Then we lay out a clear MCP-based architecture plan, from data sources to servers to model context structure.

You’ll know exactly what goes where, and why.

We Build the First Agent End-to-End

Our team prototypes and builds a working agent that connects to your real tools using MCP, so you can see it in action.

We handle the messy parts: MCP servers, auth, session flows, model prompts, fallback logic, token management.

We Leave Behind a Developer-Ready System

We don’t just build for you, we build with your future devs in mind. We document everything, write clean, reproducible code, and set up the infra so your team can maintain or extend it easily.

Think of it like leaving behind a blueprint and a toolkit.

We Help You Get Started, Fast

Once the system is live, we help onboard your team, suggest next use cases, and support you as you scale. Whether you're using it internally or as part of a customer-facing product, we’re here to help you ship confidently.

Final Notes

If you’ve made it this far, you’re already thinking differently about how AI can transform your business. The Model Context Protocol (MCP) is fast emerging as a standard protocol for building AI systems that are context-aware, action-ready, and deeply integrated with your tools and workflows. 

It frees you from brittle integrations and opens up a plug-and-play universe where agents can think and act across systems.

Whether you’re building internal copilots, customer-facing AI products, or infrastructure for autonomous agents, MCP gives you a modular way to bring in the context your models need.

It is still early. But the building blocks are here. And if you're building with LLMs, now's a good time to rethink the stack. 

Get in touch with us — we’d love to share how MCP can transform your business.