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Lightdash MCP Server

@poddubnyoleg

代码仓库

基本信息

分类

数据与分析

传输方式

stdio

作者

poddubnyoleg

提交者

pd

关于 Lightdash MCP Server

MCP server for Lightdash analytics platform. Enables AI assistants like Claude to discover data, execute queries, create charts, and manage dashboards programmatically. Supports Lightdash Cloud and self-hosted instances.

#lightdash#analytics#mcp#claude#business-intelligence#data-visualization#dashboards#python

Lightdash MCP Server 配置

使用下面的配置,将此服务器添加到你的 MCP 客户端。

{
  "mcpServers": {
    "lightdash": {
      "command": "uvx",
      "args": [
        "lightdash-mcp"
      ],
      "env": {
        "LIGHTDASH_TOKEN": "<YOUR_TOKEN>",
        "LIGHTDASH_URL": "https://app.lightdash.cloud"
      }
    }
  }
}

Lightdash MCP Server 工具

工具列表暂未提供

当前收录暂时没有可展示的工具列表，不代表服务没有工具。请查阅项目文档了解支持的能力。

点击上方按钮,在临时沙箱中运行此服务器并实时获取工具列表。

概览

What is Lightdash Mcp?

Lightdash Mcp is a Model Context Protocol (MCP) server that connects AI assistants like Claude and Cursor to Lightdash analytics. It enables LLMs to discover data, create charts, and manage dashboards programmatically.

How to use Lightdash Mcp?

Install via pip install lightdash-mcp, uvx lightdash-mcp, or pipx run lightdash-mcp. Set environment variables LIGHTDASH_TOKEN and LIGHTDASH_URL, then run lightdash-mcp. Optionally configure Cloudflare Access or Google Cloud IAP. Add the server to MCP clients (e.g., Claude Desktop) by providing the command and environment variables.

Key features of Lightdash Mcp

  • Discovery: explore data catalogs, tables, and schemas
  • Querying: execute queries with filters, metrics, and aggregations
  • Chart management: create, read, update, delete charts
  • Dashboard management: build and manage dashboards with tiles and filters
  • Resource organization: create and manage spaces for content

Use cases of Lightdash Mcp

  • Allow an AI assistant to explore available tables and schemas in Lightdash
  • Automate chart creation and configuration from natural language prompts
  • Duplicate and modify existing dashboards programmatically
  • Run ad-hoc metric queries against any explore in a project
  • Organize analytics content by creating spaces and moving resources

常见问题

1

How do I obtain a Lightdash token?

Go to **Settings** → **Personal Access Tokens** in your Lightdash instance, click **Generate new token**, and copy the token (starts with `ldt_`).

2

What are the prerequisites?

Python 3.10+, a Lightdash instance (Cloud or self-hosted), and a Lightdash Personal Access Token.

3

How do I handle authentication errors?

Verify your `LIGHTDASH_TOKEN` is correct and starts with `ldt_`, check the token hasn’t expired, and ensure you have the necessary permissions in Lightdash.

4

How do I set up Google Cloud IAP support?

Install with the `[iap]` extra (`pip install lightdash-mcp[iap]`), set `IAP_ENABLED=true`, and ensure the runtime service account has `roles/iam.serviceAccountTokenCreator` on itself and `roles/iap.httpsResourceAccessor` on the Cloud Run service.

5

How can I add a new tool to the server?

Create a new Python file in `lightdash_mcp/tools/` with a `TOOL_DEFINITION` and a `run` function. The server automatically discovers and registers the tool on restart.

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