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Model Context Protocol (MCP): Everything You Need To Know.

By
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AI assistants like ChatGPT or Claude can write brilliant code, solve complex problems, write all kinds of content, and have an answer to almost every question. However, before now they couldn’t do something as simple as sending an email or booking a meeting. A classic situation of all talk and no action.

And that's the problem the Model Context Protocol (MCP) was designed to solve.

If you have been hearing a lot about Model Context Protocol (MCP) but don’t really get what all the fuss is about, then you have landed in the right spot.

In this article, we'll break down everything about the model context protocol (MCP). We'll also explore what problems it solves, how it works behind the scenes, and most importantly, how you can start using MCPs easily to connect your AI assistants to over 300+ tools without writing a single line of code.

Now let's dive in

What is the Model Context Protocol (MCP)?

The model context protocol (MCP) is a universal translator that lets your AI assistant communicate with your everyday apps like calendar, email, CRM, and all your other tools.

The Model Context Protocol (MCP) was introduced by Anthropic in late 2024 to connect AI models with external tools, data, and applications.

Think of it like the USB-C for AI

I have heard a lot of people call it Anthropic’s MCP protocol, but I find it quite funny because calling it “MCP protocol” is like saying “ATM machine.”

Our professor of vibe coding here at Activepieces, Jordan, once joked about MCP being Massive Confusion Protocol. This is because when it came out, even Anthropic Claude didn't know what MCPs were. There were like a billion abbreviations from MCPs from both Google searches and AI models.

However, one of the most confusing ones is the Message Control Protocol. Model Context Protocol is not the same as Message Control Protocol which is a popular term in network engineering.

Our focus here is on the AI-based MCP standard that Anthropic and others are championing.

What Problem Does MCP Solve?

  • LLM Isolation: Large language models (LLMs) like ChatGPT or Claude are very smart and know everything, but can't connect to real-world systems. For example, ChatGPT or Claude might draft an email for you, but you still have to copy-paste and send it manually. They could suggest a meeting time, but couldn’t actually add it to your calendar.So this made us quite far away from achieving that Jarvis-level AI system

  • The Integration Nightmare (NxM Problem): Before MCP, connecting AI to external tools was possible but difficult and messy. Every AI model (let's call it “N”) needed custom code to integrate with each app or data source (let's call it “M”). So, for example, if you had 5 AI systems and 5 tools, you might need 25 different custom integrations. This led to repetitive work for each new combo. In practice, only big tech or highly motivated dev teams attempted such integrations, and each one was one-off.

  • Previous Workarounds Fell Short: OpenAI’s 2023 function calling and ChatGPT plug-ins allowed some tool use, but those were vendor-specific and didn’t talk to each other. Each platform had its siloed way of doing things, and that still left smaller tools or other AI models out.

Model Context Protocols (MCP) addresses these challenges by providing one unified protocol for connecting any AI to any tool, ending the issue of incompatible APIs.

Instead of writing custom integrations for every new AI or app, developers can now build one MCP server and integrate everywhere. End users, on the other hand, can just connect to the server, add any tool they want, and the AI will be able to chat with that tool to carry out tasks.

How Does the Model Context Protocol Work?

In this section, we will look at all the moving parts that make up Model Context Protocols

MCP uses a simple client–server architecture over HTTP (technically built on JSON-RPC).

You can relate it to a phone conversation: the AI side (client) calls up the tool side (server) and they exchange information in a structured way.

However, let's look at each component of the Model Context Protocol (MCP) in detail

MCP Clients (AI Assistants):

An MCP client is essentially any LLM that wants to use external tools. This could be AI assistants like Claude, an AI coding partner like Cursor, or Windsurf. The client’s job is to initiate requests. For example, if you type in Claude, “hey, I need the list of upcoming events from the calendar,” or “please run this SQL query for me.” Claude (the client) will reach out to an MCP server and send these requests in a standardized format to fetch that info.

Therefore, any tool that is connected to the MCP server can be accessed by the AI assistant.

MCP Servers (Tool Connectors):

An MCP server is like a wrapper or middleware. Each server might offer a set of tools to the AI client. For example, a Google Calendar MCP server could show tools like “List my events” or “Create a new event”. These are tasks that you can ask your AI chatbot to carry out for you using natural language.

The server takes the AI’s request (e.g., “createEvent” with some details), translates it into the actual API calls actions behind the scenes, and then returns the results to the AI in a format it understands.

When MCP servers were first released, they were used to connect individual tools. For example, you can connect Slack, Google Sheets, or Notion. This is great, but it still had some limitations, like the inability to trigger multi-app workflows or turn your custom workflows into mini MCP tools. This led to the development of MCP flows by Activepieces.

Tools and Resources:

  • Tools: These are actions the AI can instruct the server to perform (think of functions like sendEmail, addCalendarEvent, queryDatabase).
  • Resources: These are pieces of data or content that the server can provide. For instance, a file server can share the content of a file, or a knowledge base server can return a document snippet.

Security and Control:

Because MCP potentially lets AI perform powerful actions, security is a big concern. The architecture usually involves a host (like the main AI app or platform) that can approve ****what the AI is allowed to do. For example, you might give Claude access only to a specific Google Drive folder via an MCP server. The host server ensures it won’t suddenly access your entire system. However, not all MCP servers are secure. This is why you need to check for highly secure SOC 2 Type II certified MCP servers like Activepieces MCP server

Real-World Applications of MCP

How is MCP actually being used? Let’s look at some real-world examples and use cases.

You may also be interesting in learning about 10 MCP usecases using Claude

  • Personal Productivity (Your AI Assistant at Work): MCP can turn a chatty AI into a personal assistant. For example, a calendar MCP enables your AI to send a meeting invite with Claude using a natural language prompt. With an Email MCP, it could send emails on your behalf (no more copy-paste).

  • Software Development and DevOps: Several developer tools have embraced MCP to enhance coding assistants. IDEs like Cursor integrated MCP so that an AI coding assistant can fetch real-time code context, browse your repository, or even commit changes. Think of it as pair programming with an assistant who can actually open files, run tests, or search the codebase when needed. This leads to smarter code suggestions and even automating routine coding tasks.

  • Business Process Automation: For example, MCP can pull data from proprietary documents, CRM systems, and knowledge bases. This means an employee could **directly query an AI, “What’s the status of Client X’s order?” and the AI can access databases to answer or even update records.

  • Multi-Tool Workflows (Agentic AI): One exciting aspect is using the MCP flow to accomplish complex tasks. MCP flows enable you to trigger multi-app AI automation workflows from your AI assistant. For example, if you have a flow that extracts content ideas from a sheet, writes with AI then posts on Twitter, you can connect that flow as a mini tool to your AI assistant. So you can tell Claude, for example, “Call my Twitter post flow,” and that entire workflow will be triggered with a success message returned. If you want to see an example, check out these 5 MCP flows that can be built with Airtable.

Benefits of Using MCP (Why Use MCP for AI Integration?)

  • Build Once, Use Anywhere: The biggest win with MCP is standardization. Developers can write an integration once (e.g., an MCP server for Gmail), and any AI or app that has enabled MCP can use it immediately. This reduces duplicate work. Instead of every AI platform writing its own Gmail connector, the community can maintain one solid MCP connector. It’s efficient and accelerates innovation. When someone builds a new MCP tool, everyone benefits. Activepieces is also currently running an MCP bounty where you build MCPs for a tool and get paid for it. Learn more here: Activepieces MCP bounty

  • Expanded Capabilities for AI (LLM Superpowers): By giving AI access to real-world data and actions, MCP expands what AI assistants can do. LLMs are no longer limited to training data, an AI can fetch recent information (your latest sales numbers, today’s server status, etc.) and even take actions on your behalf. This leads to more relevant answers and the ability to complete tasks end-to-end.

  • Faster Development and Integration: If you’re a developer or a company looking to add AI features, MCP can save you huge amounts of time. Rather than writing custom integration code for each tool (and maintaining it), you can lean on existing MCP servers.

  • Community and Open-Source Ecosystem: MCP’s open nature has spurred a vibrant community. There’s already a growing library of open-source MCP servers for popular systems (Google Drive, Slack, GitHub, databases, you name it). New contributions are added daily, and as of 2025, there are thousands of MCP servers available publicly. This community-driven growth means you’re likely to find pre-built solutions for many of your needs. You can check out the largest opensource Model Context Protocol (MCP) ecosystem

MCP vs. Traditional APIs and Integrations

If you’re thinking, “Isn’t this just an API?”, you’re a bit right, but here is the difference. Traditional APIs are each unique. Google Calendar has one API, Slack has another, etc.

Each API has different endpoints, auth, and data formats, so integrating them all into an AI agent is like learning dozens of languages. Model Context Protocols (MCP) is like a universal API layer on top of those. It standardizes how the AI talks to any service, so the AI doesn’t need to know if, it’s calling Google or Slack . It just says “fetch events” or “send message” in the MCP language, and the server knows which tool or function to trigger. The MCP server then translates that to the specific API calls. This is why MCP is more than just another API

Let's look at it using a real-world analogy. Consider old-school phone operators versus dialing every department directly.

Old way (Traditional API): You need to know each department’s number (each API) to get what you need.

New way (MCP ): You dial the operator (common protocol) and just ask for the department by name. The operator handles connecting you based on the context of your request.

However, it is important to note that MCP doesn’t replace existing APIs; it often uses them behind the scenes. The MCP server is usually a middleman that calls the real API of a service. For developers, this means you still respect the rules of the underlying API (rate limits, auth scopes), but you present them to the AI in a uniform way.

So, MCP is complementary to traditional APIs, not a competitor; it just makes them far easier to use in the AI context.

Model Context Protocol (MCP) Adoption and Future Outlook

  • Anthropic’s Initiative and Early Adopters: MCP started with Anthropic (the developers of Claude), but it didn’t stay there. Right from the announcement, some companies jumped on board as early adopters. Developer tool companies such as Cursor began experimenting with MCP to enhance their AI features. This early interest validated that MCP was solving a real pain point across industries.
  • OpenAI and Others Joining In: In a significant turn, OpenAI officially adopted MCP recently, to incorporate the MCP into products like ChatGPT and their Agents SDK. So, for those wondering, “Can ChatGPT use MCP?”, the answer is yes**.** OpenAI recognized that standardizing AI tool connectivity benefits everyone, not just a single platform.
  • Microsoft: Even platform providers like Microsoft are embracing MCP. MCP is now available in Microsoft Copilot Studio

Activepieces MCP: The Largest Open-Source Model Context Protocol Ecosystem.

You don’t have to build your own MCP servers or write custom code to connect your tools to AI. Simply sign up for free, go to the MCP section, and add the tools you want your AI to have access to. Activepieces is also MIT licensed and highly prioritizes security when giving AI access to your tools.

Activepieces also goes beyond just connecting individual tools to connecting complex multi-app workflows to your AI system using MCP flow.

How to Connect To Activepieces MCP Server

Prerequisites

Step 1: Configure the MCP Server

For Claude

  • Go to Activepieces > MCP > Client Setup Instructions
  • Open the Claude desktop
  • Open Settings: Click the menu and select Settings > Developer
  • Configure MCP Server: Click Edit Config and paste the configuration below
  • Save and Restart: Save the config and restart Claude Desktop

Server Configuration

{
  "mcpServers": {
    "Activepieces": {
      "command": "npx",
      "args": [
        "-y",
        "mcp-remote",
        "https://cloud.activepieces.com/api/v1/mcp/**********************/sse"
      ]
    }
  }
}

For Cursor:

  • Open Cursor → Settings → Features → MCP
  • Click “+ Add New Global MCP Server”
  • Add server configuration
  • Restart Cursor

Server Configuration

{

"mcpServers": {

"Activepieces": {

"url": "https://cloud.activepieces.com/api/v1/mcp/e2fB6FOygN2Y2qEsbevVU/sse"

}

}

}

For Winsurf

  1. Open setting: Use either method:
    • Go to Windsurf > Settings > Advanced Settings
    • Open Command Palette and select the Windsurf Setting Page
  2. Navigate to Cascade: Select Cascade in the sidebar
  3. Add server: Click Add Server > Add custom server +
  4. Paste the configuration below and save

Server Configuration

{

"mcpServers": {

"Activepieces": {

"url": "https://cloud.activepieces.com/api/v1/mcp/e2fB6FOygN2Y2qEsbevVU/sse"

}

}

}

Step 4: Test Your Integration

Try a sample query like:

"Can you show me the first 5 records in the Projects table from my Airtable base?"