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Vibe Coding with the MCP Server

"Vibe Coding" is an interactive, conversational approach to development where you use natural language to generate code or specifications. Intugle embraces this by allowing you to serve your semantic layer through an MCP (Model Context Protocol) server.

This turns your data into a "self-describing" resource that an AI assistant can understand, allowing you to "vibe" with your data to create data product specifications without writing the JSON by hand.

MCP Prompts

This workflow uses the capability of MCP servers to expose prompts to an LLM client. To use this feature, your LLM client must support the Model Context Protocol and read prompts from the MCP Server.

1. Setting up the MCP Server​

Once you have built your semantic layer using the KnowledgeBuilder, you can easily expose it as a set of tools for an AI assistant by starting the built-in MCP server.

Starting the Server​

To start the server, run the following command in your terminal from your project's root directory:

intugle-mcp

This will start a server on localhost:8000 by default. You should see output indicating that the server is running and that the semantic_layer and adapter services are mounted.

Connecting from an MCP Client​

With the server running, you can connect to it from any MCP-compatible client. The endpoint for the semantic layer is:

http://localhost:8000/semantic_layer/mcp

Popular clients that support MCP include AI-powered IDEs and standalone applications. Here’s how to configure a few of them:

2. Generating Data Products with create-dp​

The MCP server exposes a powerful prompt called create-dp. This prompt is designed to take your natural language request and convert it directly into a valid product_spec dictionary.

How to Use It​

In your MCP-compatible client, you can invoke the prompt and provide your request. In most clients, this is done by typing / followed by the prompt name.

/create-dp show me the top 5 patients with the most claims
Client-Specific Commands

The exact command to invoke a prompt (e.g., using / or another prefix) can vary between clients. Be sure to check the documentation for your specific tool.

The AI assistant, connected to your MCP server, will understand the request, use the get_tables and get_schema tools to find the patients and claims tables, and generate the following product_spec:

{
  "name": "top_5_patients_by_claims",
  "fields": [
    {
      "id": "patients.first",
      "name": "first_name"
    },
    {
      "id": "patients.last",
      "name": "last_name"
    },
    {
      "id": "claims.id",
      "name": "number_of_claims",
      "category": "measure",
      "measure_func": "count"
    }
  ],
  "filter": {
    "sort_by": [
      {
        "id": "claims.id",
        "alias": "number_of_claims",
        "direction": "desc"
      }
    ],
    "limit": 5
  }
}

This workflow allows you to stay in your creative flow, rapidly iterating on data product ideas by describing what you want in plain English.

Stay Tuned! We will be updating this section soon to include how to vibe code with other pieces of Intugle.