What is Lightdash Mcp Server

lightdash-mcp-server is a Model Context Protocol (MCP) server that provides access to Lightdash’s API, enabling AI assistants to interact with Lightdash data through a standardized interface.

Use cases

Use cases for lightdash-mcp-server include integrating AI assistants with Lightdash for data analysis, automating reporting processes, and enhancing data visualization capabilities within applications.

How to use

To use lightdash-mcp-server, install it via Smithery or manually using npm. Configure your Lightdash API credentials in a .env file, then start the server and edit your MCP configuration JSON to include the necessary commands and environment variables.

Key features

Key features include tools to list projects, spaces, charts, and dashboards in Lightdash, retrieve custom metrics, and access project catalogs and charts/dashboards as code.

Where to use

undefined

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)

1. Start the MCP server:

npx lightdash-mcp-server

2. Edit your MCP configuration json:

HTTP Transport (Streamable HTTP)

1. 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.

2. 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:

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

1. Fork the repository
2. Create your feature branch
3. Run tests and linting: npm run lint
4. Commit your changes
5. Push to the branch
6. Create a Pull Request