Lightdash MCP Server

lightdash-mcp-server

A MCP(Model Context Protocol) server that accesses to Lightdash.

This server provides MCP-compatible access to Lightdash's API, allowing AI assistants to interact with your Lightdash data through a standardized interface.

Features

Available tools:

list_projects - List all projects in the Lightdash organization
get_project - Get details of a specific project
list_spaces - List all spaces in a project
list_charts - List all charts in a project
list_dashboards - List all dashboards in a project
get_custom_metrics - Get custom metrics for a project
get_catalog - Get catalog for a project
get_metrics_catalog - Get metrics catalog for a project
get_charts_as_code - Get charts as code for a project
get_dashboards_as_code - Get dashboards as code for a project

Quick Start

Installation

Installing via Smithery

To install Lightdash MCP Server for Claude Desktop automatically via Smithery:

npx -y @smithery/cli install lightdash-mcp-server --client claude

Manual Installation

npm install lightdash-mcp-server

Configuration

LIGHTDASH_API_KEY: Your Lightdash PAT
LIGHTDASH_API_URL: The API base URL

Usage

The lightdash-mcp-server supports two transport modes: Stdio (default) and HTTP.

Stdio Transport (Default)

Start the MCP server:

npx lightdash-mcp-server

Edit your MCP configuration json:

...
    "lightdash": {
      "command": "npx",
      "args": [
        "-y",
        "lightdash-mcp-server"
      ],
      "env": {
        "LIGHTDASH_API_KEY": "<your PAT>",
        "LIGHTDASH_API_URL": "https://<your base url>"
      }
    },
...

HTTP Transport (Streamable HTTP)

Start the MCP server in HTTP mode:

npx lightdash-mcp-server -port 8080

This starts the server using StreamableHTTPServerTransport, making it accessible via HTTP at http://localhost:8080/mcp.

Configure your MCP client to connect via HTTP:

For Claude Desktop and other MCP clients:

Edit your MCP configuration json to use the url field instead of command and args:

...
    "lightdash": {
      "url": "http://localhost:8080/mcp"
    },
...

For programmatic access:

Use the streamable HTTP client transport:

import { Client } from '@modelcontextprotocol/sdk/client/index.js';
import { StreamableHTTPClientTransport } from '@modelcontextprotocol/sdk/client/streamableHttp.js';

const client = new Client({
  name: 'my-client',
  version: '1.0.0'
}, {
  capabilities: {}
});

const transport = new StreamableHTTPClientTransport(
  new URL('http://localhost:8080/mcp')
);

await client.connect(transport);

Note: When using HTTP mode, ensure the environment variables LIGHTDASH_API_KEY and LIGHTDASH_API_URL are set in the environment where the server is running, as they cannot be passed through MCP client configuration.

See examples/list_spaces_http.ts for a complete example of connecting to the HTTP server programmatically.

Development

Available Scripts

npm run dev - Start the server in development mode with hot reloading (stdio transport)
npm run dev:http - Start the server in development mode with HTTP transport on port 8080
npm run build - Build the project for production
npm run start - Start the production server
npm run lint - Run linting checks (ESLint and Prettier)
npm run fix - Automatically fix linting issues
npm run examples - Run the example scripts

Contributing

Fork the repository
Create your feature branch
Run tests and linting: npm run lint
Commit your changes
Push to the branch
Create a Pull Request

Deploy Server

HTTP connection URL

security – no known vulnerabilities

license - permissive license

quality - confirmed to work

How are these scores calculated?

remote-capable server

The server can be hosted and run remotely because it primarily relies on remote services or has no dependency on the local environment.

MCP-compatible server that enables AI assistants to interact with Lightdash analytics data, providing tools to list and retrieve projects, spaces, charts, dashboards, and metrics through a standardized interface.

Related Resources

Reddit Discussion about this server

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