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What is MCP? Complete Developer Guide to Model Context Protocol

Learn everything about MCP (Model Context Protocol) — what it is, how it works, how to set up MCP servers, and why it's transforming AI-powered development in 2026.

DevTk.AI 2026-02-19

If you’ve been building with AI in 2026, you’ve almost certainly encountered MCP — the Model Context Protocol. It’s the open standard that lets AI assistants like Claude, ChatGPT, and Gemini connect to external tools and data sources through a single, universal interface. Think of it as USB-C for AI integrations: one protocol to connect them all.

In this guide, we’ll walk through everything you need to know about MCP as a developer — what it is, how it works under the hood, how to set it up, and how to build your own MCP servers.

What is the Model Context Protocol (MCP)?

MCP (Model Context Protocol) is an open standard originally created by Anthropic and released in late 2024. It defines a universal way for AI models to discover and interact with external tools, data sources, and services.

Before MCP, every AI application had to build its own integration layer. Claude had its own tool-calling format, ChatGPT had its own plugin system, and every IDE extension reinvented the wheel. MCP changes this by providing a single, standardized protocol that any AI client can use to communicate with any compatible server.

The key idea is simple: separate the AI model from the tools it uses. An MCP server exposes capabilities (tools, resources, prompts), and any MCP-compatible client can discover and use them — regardless of which AI model is running underneath.

MCP vs. Traditional API Integrations

AspectTraditional ApproachMCP Approach
Integration effortCustom code per tool per appWrite once, use everywhere
DiscoveryHardcoded endpointsDynamic capability discovery
Auth & transportVaries per serviceStandardized (stdio, SSE, HTTP)
EcosystemFragmentedShared registry of servers

How MCP Works: Architecture Overview

MCP follows a client-server architecture with three main concepts:

The Three Primitives

  1. Tools — Functions the AI can call. For example, a GitHub MCP server might expose tools like create_issue, search_repos, or create_pull_request. The AI model decides when and how to invoke these tools based on user intent.

  2. Resources — Read-only data the AI can access. Think of these as files, database records, or any structured data the model might need for context. A filesystem MCP server exposes files as resources; a database server exposes query results.

  3. Prompts — Reusable prompt templates that a server can provide. These are pre-built instructions the AI can use for specific workflows, like “summarize this codebase” or “review this pull request.”

Communication Flow

Here’s what happens when you ask Claude to “create a GitHub issue”:

User → MCP Client (Claude Desktop)
         → discovers available tools from GitHub MCP Server
         → AI decides to call `create_issue` tool
         → MCP Client sends JSON-RPC request to GitHub MCP Server
         → Server executes the action via GitHub API
         → Response flows back to AI
         → AI presents the result to user

MCP uses JSON-RPC 2.0 as its wire protocol. Transport can happen over:

  • stdio — Local servers running as child processes (most common for desktop apps)
  • SSE (Server-Sent Events) — Remote servers accessible over HTTP
  • Streamable HTTP — The newer transport for production deployments

Why MCP Matters

1. One Integration, Every AI App

Before MCP, if you built a tool integration for Claude, it wouldn’t work with ChatGPT or Cursor. With MCP, you write one server and it works everywhere — Claude Desktop, VS Code with Copilot, Cursor, Windsurf, and any other MCP-compatible client.

2. Ecosystem Effects

Because MCP is an open standard, it created a massive shared ecosystem. As of early 2026, there are over 8,600 community-built MCP servers covering everything from databases and cloud providers to niche developer tools. You can browse many of them in our MCP Server Directory.

3. Composability

You can connect multiple MCP servers to a single AI client simultaneously. Claude can access your filesystem, query your database, search the web, and manage your GitHub repos — all in one conversation, all through MCP.

4. Security Model

MCP servers run with explicit user consent. The user decides which servers to connect, and servers only expose the capabilities they’re designed for. There’s no ambient authority — the AI must request tool use, and the client can gate approvals.

Getting Started: Setting Up MCP with Claude Desktop

The fastest way to experience MCP is with Claude Desktop. Here’s a step-by-step setup.

Step 1: Install Claude Desktop

Download Claude Desktop from claude.ai/download if you haven’t already. Make sure you’re on the latest version.

Step 2: Locate the Config File

Claude Desktop reads MCP server configuration from a JSON file:

  • macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
  • Windows: %APPDATA%\Claude\claude_desktop_config.json

Step 3: Add an MCP Server

Open the config file and add a server. Here’s an example that adds the filesystem MCP server to give Claude access to a project directory:

{
  "mcpServers": {
    "filesystem": {
      "command": "npx",
      "args": [
        "-y",
        "@modelcontextprotocol/server-filesystem",
        "/Users/you/projects/my-app"
      ]
    }
  }
}

Step 4: Add More Servers

You can add multiple servers. Here’s a more complete configuration with GitHub and Brave Search:

{
  "mcpServers": {
    "filesystem": {
      "command": "npx",
      "args": [
        "-y",
        "@modelcontextprotocol/server-filesystem",
        "/Users/you/projects"
      ]
    },
    "github": {
      "command": "npx",
      "args": ["-y", "@modelcontextprotocol/server-github"],
      "env": {
        "GITHUB_PERSONAL_ACCESS_TOKEN": "ghp_your_token_here"
      }
    },
    "brave-search": {
      "command": "npx",
      "args": ["-y", "@modelcontextprotocol/server-brave-search"],
      "env": {
        "BRAVE_API_KEY": "your_brave_api_key"
      }
    }
  }
}

Step 5: Restart and Verify

Restart Claude Desktop. You should see a hammer icon in the chat input area indicating available MCP tools. Click it to see all discovered tools from your configured servers.

Need help generating this config? Use our MCP Manifest Generator to build your claude_desktop_config.json visually — no manual JSON editing required. You can also validate an existing config with the MCP Config Validator.

Here are some of the most widely used MCP servers in the ecosystem:

Official Reference Servers

ServerPackageWhat It Does
Filesystem@modelcontextprotocol/server-filesystemRead, write, and search files on your local machine
GitHub@modelcontextprotocol/server-githubManage repos, issues, PRs, and code search
PostgreSQL@modelcontextprotocol/server-postgresQuery and inspect PostgreSQL databases
Brave Search@modelcontextprotocol/server-brave-searchWeb search via the Brave Search API
Memory@modelcontextprotocol/server-memoryPersistent knowledge graph for AI memory

Community Favorites

ServerWhat It Does
PlaywrightBrowser automation — navigate, screenshot, interact with web pages
NotionRead and update Notion pages and databases
SlackSend messages, search channels, manage Slack workspaces
LinearManage issues and projects in Linear
SentryQuery error reports and performance data
SupabaseManage Supabase projects, run SQL, handle auth

Browse the full list in our MCP Server Directory, which includes GitHub star counts, categories, and direct links to documentation.

Building Your Own MCP Server

If your tool or service doesn’t have an MCP server yet, building one is straightforward. The official SDKs are available in TypeScript, Python, Java, C#, and Kotlin.

Quick Example: TypeScript MCP Server

Here’s the skeleton of a simple MCP server using the TypeScript SDK:

import { McpServer } from "@modelcontextprotocol/sdk/server/mcp.js";
import { StdioServerTransport } from "@modelcontextprotocol/sdk/server/stdio.js";
import { z } from "zod";

const server = new McpServer({
  name: "my-custom-server",
  version: "1.0.0",
});

// Define a tool
server.tool(
  "greet_user",
  "Greets a user by name",
  { name: z.string().describe("The user's name") },
  async ({ name }) => ({
    content: [{ type: "text", text: `Hello, ${name}! Welcome to MCP.` }],
  })
);

// Start the server
const transport = new StdioServerTransport();
await server.connect(transport);

Install the SDK, build your server, and add it to your Claude Desktop config:

{
  "mcpServers": {
    "my-server": {
      "command": "node",
      "args": ["path/to/your/build/index.js"]
    }
  }
}

If You Have an Existing OpenAPI Spec

Many teams already have REST APIs with OpenAPI (Swagger) specifications. Instead of building an MCP server from scratch, you can convert your OpenAPI spec directly into MCP tool definitions using our OpenAPI to MCP Converter. It parses your spec and generates the corresponding MCP server configuration, saving significant development time.

The MCP Ecosystem in 2026

MCP’s growth since its November 2024 launch has been remarkable:

  • 8,600+ community servers listed across registries like mcp.so, Smithery, and Glama
  • Adopted by major players: OpenAI added MCP support to ChatGPT Desktop and the Agents SDK. Google integrated MCP into Gemini and the Agent Development Kit. Microsoft added MCP support to Copilot Studio and the Azure AI Foundry Agent Service.
  • IDE integration: Cursor, Windsurf, VS Code (via Copilot), and JetBrains IDEs all support MCP natively.
  • Remote servers emerging: The shift from local stdio-based servers to remote, authenticated MCP servers (using OAuth 2.1 and streamable HTTP) is unlocking enterprise deployments.
  • Standardization efforts: The MCP specification continues to evolve, with the community working on better auth flows, server discovery, and capability negotiation.

What’s Next for MCP

The protocol is moving toward remote-first architectures. Early MCP servers ran as local Node.js processes, but the ecosystem is shifting to hosted servers that can be shared across teams. Expect to see:

  • MCP server marketplaces where you can one-click install servers
  • OAuth-based auth replacing manual API key configuration
  • Agent-to-agent communication built on MCP primitives
  • Enterprise governance tools for managing which MCP servers are approved for use

Getting Started Today

If you’re new to MCP, here’s a practical path forward:

  1. Try it out — Set up Claude Desktop with the filesystem and GitHub servers. Experience the workflow firsthand.
  2. Explore the ecosystem — Browse our MCP Server Directory to find servers relevant to your stack.
  3. Generate your config — Use the MCP Manifest Generator to build your configuration file without touching JSON manually.
  4. Validate your setup — Run your config through the MCP Config Validator to catch errors before they cause silent failures.
  5. Build your own — If you have an internal tool or API, wrap it in an MCP server. Start with the TypeScript or Python SDK, or convert an existing OpenAPI spec with our OpenAPI to MCP Converter.

MCP is quickly becoming the foundational layer for how AI assistants interact with the world. Whether you’re building AI-powered developer tools, integrating AI into existing workflows, or just trying to get more out of Claude — understanding MCP is no longer optional. It’s essential infrastructure for the AI-native developer stack.

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