{
  "id": 2728733,
  "title": "How to Create an MCP Server: Tutorial",
  "url": "https://urgent.news/2026/08/23/how-to-create-an-mcp-server-tutorial",
  "topic": "tech",
  "section": "Tech",
  "published": "2026-08-23T05:27:07.000Z",
  "source": {
    "name": "Dev.to",
    "slug": "dev-to",
    "url": "https://dev.to/santattech/how-to-create-an-mcp-server-tutorial-5dj5"
  },
  "original_language": "en",
  "account": "Model Context Protocol (MCP) enables an AI assistant to connect with external systems like APIs or databases through a structured format. In this tutorial, we'll explore how to build an MCP server that acts as a bridge between an AI agent and a Todo Management API called TodoHub.\n\nOur architecture consists of a User sending a request to an AI Agent (Kiro, Codex, Claude), which then calls an MCP tool provided by the TodoHub MCP Server. The server communicates with the REST API of TodoHub to fulfill the request.\n\nThe MCP server provides AI-friendly tools, such as \"get_todo\" and \"create_todo\", which simplify the API interaction for the AI agent. These tools translate the AI-friendly input parameters into the internal application parameters required by the REST API.\n\nTo set up the MCP server, we create an mcp.json configuration file that specifies the available tools, their commands, arguments, and necessary environment variables. For our example, we configure the \"todohub\" MCP server to use stdio communication and provide the required environment variables such as the TodoHub URL and API key.\n\nWhen a user sends a request like \"Show me todo 123\", the AI agent understands the intent and identifies the required tool, \"get_todo\", with the input parameter \"todo_id = 123\". The MCP server receives this request and executes the equivalent GET request to the TodoHub REST API: GET https://todos.example.com/api/todos/123.\n\nTodoHub responds with the requested todo data, which is then returned to the user by the MCP server. This end-to-end flow demonstrates how the MCP server abstracts the complexity of the underlying REST API, providing a simpler and more AI-friendly interface for the assistant to interact with.",
  "summary": "Model Context Protocol (MCP): Building an AI-to-API Bridge 1. What Is MCP? Model Context Protocol (MCP) allows an AI assistant such as Kiro, Codex, Claude, or another MCP-compatible agent to interact with external systems in a structured way. A useful mental model is: AI Agent --> MCP Client --> MCP Server --> External API / Database / Application For this example, assume we have an internal Todo…",
  "key_points": [
    "Model Context Protocol (MCP) connects AI assistants with external systems via structured format.",
    "MCP server acts as bridge between AI agent and TodoHub REST API."
  ],
  "editors_take": "This development streamlines AI agent interactions with external systems by providing a structured format for communication, abstracting the complexity of underlying APIs for simpler integration.",
  "illustration": null,
  "coverage": {
    "outlets": 1,
    "also_reported_by": []
  },
  "ai_generated": true,
  "disclaimer": "Summaries, key points and the editor’s take are written by software from other outlets’ reporting and may contain errors — always check the linked original."
}