MCP: The Universal Plug-and-Play Standard for AI Tools

If you have been building AI agents or following the AI tools space, you have probably encountered the term MCP. The Model Context Protocol is one of those infrastructure developments that does not make for exciting headlines but fundamentally changes how AI tools work together. Think of it as USB for AI: a universal standard that lets any AI agent connect to any external tool without custom integration code.

Here is what MCP is, why it matters, and how to use it.

What Is the Model Context Protocol?

MCP is an open standard, originally developed by Anthropic and now adopted across the industry, that defines how AI models interact with external tools, data sources, and services. Before MCP, every combination of AI framework and external tool required custom integration code. Want to connect LangChain to your database? Write a custom integration. Want that same database accessible from CrewAI? Write another one. Different framework, different code, same database.

MCP eliminates this multiplication problem. A tool built to the MCP specification works with any MCP-compatible AI framework. Build your database connector once as an MCP server, and it works with LangChain, CrewAI, Claude, and every other MCP-compatible system.

The protocol defines three core primitives:

• Tools. Functions the AI can call to take actions (query a database, send an email, create a file).
• Resources. Data sources the AI can read (files, database records, API responses).
• Prompts. Reusable prompt templates that standardize how the AI interacts with specific tools.

Why MCP Matters

Before MCP

Imagine you are building an AI agent that needs to access your company's Jira, Slack, GitHub, and internal database. Without MCP, you need:

• Framework-specific integration for Jira
• Framework-specific integration for Slack
• Framework-specific integration for GitHub
• Framework-specific integration for your database

That is four custom integrations. Now imagine you want to switch from LangChain to CrewAI. You need to rewrite all four integrations. And every other team building AI tools faces the exact same problem, duplicating effort across the entire industry.

After MCP

With MCP, each tool is built once as an MCP server:

• MCP server for Jira
• MCP server for Slack
• MCP server for GitHub
• MCP server for your database

Any MCP-compatible framework connects to all of them immediately. Switch frameworks? Your tool servers keep working. Someone else built an MCP server for a service you use? You can plug it in without writing any code. The ecosystem grows for everyone.

The Architecture

MCP uses a client-server architecture:

AI Application (MCP Client)
    |
    |--- MCP Protocol (JSON-RPC over stdio/SSE) --->  MCP Server (Tool/Resource)
    |--- MCP Protocol --->  MCP Server (Another Tool)
    |--- MCP Protocol --->  MCP Server (Yet Another Tool)

MCP Hosts are applications like Claude Desktop, IDE extensions, or AI agents that want to use external tools.

MCP Clients maintain connections to MCP servers, handling the protocol communication. Each client typically has a one-to-one relationship with a server.

MCP Servers expose tools, resources, and prompts through the standardized protocol. They can be lightweight processes running locally or remote services accessed over the network.

The transport layer supports two modes: stdio for local servers (simple, fast, no network overhead) and Server-Sent Events (SSE) over HTTP for remote servers (works across networks, enables shared tool services).

Which Frameworks Support MCP?

Adoption has been rapid. Here is the current support landscape:

The breadth of support means that an MCP server you build today works across virtually the entire AI tool ecosystem.

How to Use MCP: A Practical Example

Let us walk through a concrete example. Suppose you want to give your AI agent access to a filesystem so it can read and write files.

Step 1: Find or Build an MCP Server

The MCP ecosystem already has servers for common use cases. The official MCP servers repository includes servers for filesystems, GitHub, Slack, PostgreSQL, Google Drive, and many more.

For a filesystem, you would use the reference filesystem server:

npx -y @modelcontextprotocol/server-filesystem /path/to/allowed/directory

Step 2: Configure Your MCP Client

In Claude Desktop, you add the server to your configuration file:

{
  "mcpServers": {
    "filesystem": {
      "command": "npx",
      "args": [
        "-y",
        "@modelcontextprotocol/server-filesystem",
        "/path/to/allowed/directory"
      ]
    }
  }
}

That is it. Claude can now read and write files in the specified directory. No custom code, no API wrappers, no framework-specific adapters.

Step 3: Use It

Once configured, the AI can discover available tools automatically and use them as needed. Ask Claude to "read the contents of config.yaml" or "create a new file called notes.txt with today's meeting summary," and it will use the filesystem MCP server to execute those actions.

Real-World MCP Use Cases

MCP is already enabling practical workflows across many domains:

Development workflows. Connect your AI agent to GitHub (for PRs and issues), your CI/CD pipeline (for build status), and your database (for schema queries). The agent can investigate bugs by correlating error logs, recent code changes, and database state.

Customer support. An MCP server for your ticketing system, combined with servers for your knowledge base and CRM, lets an AI agent handle support queries with full context. It can look up the customer, check their recent tickets, search the knowledge base, and draft a response.

Data analysis. Connect to PostgreSQL, Google Sheets, or any data source via MCP. The AI agent can query data, run analyses, and write results back, all through standardized interfaces.

Content management. MCP servers for your CMS, image storage, and social media platforms let an AI agent publish and manage content across channels.

Building Your Own MCP Server

Building an MCP server is straightforward. The official SDKs support TypeScript and Python, with community SDKs available for other languages. A minimal MCP server that exposes a single tool looks like this:

from mcp.server.fastmcp import FastMCP

mcp = FastMCP("my-tool-server")

@mcp.tool()
def get_weather(city: str) -> str:
    """Get the current weather for a city."""
    # Your implementation here
    return f"Weather in {city}: 72F, sunny"

if __name__ == "__main__":
    mcp.run()

The @mcp.tool() decorator handles all the protocol details: registering the tool, describing its parameters, and managing the communication with clients.

The Bigger Picture

MCP is doing for AI tools what HTTP did for web content and what USB did for hardware peripherals: creating a universal standard that eliminates compatibility headaches and accelerates ecosystem growth.

The practical impact is already visible. The MCP server ecosystem is growing rapidly, with community-built servers for everything from Spotify to Kubernetes to local databases. Each new server immediately benefits every MCP-compatible application and framework.

If you are building AI agents or tools, supporting MCP is no longer optional. It is the standard. And if you are evaluating AI frameworks, MCP compatibility should be near the top of your checklist. The frameworks that support MCP give you access to a growing ecosystem of tools that you do not have to build or maintain yourself.

The age of custom AI integrations for every tool-framework combination is ending. MCP is the universal connector, and it is here to stay.