Lightdash MCP Server

What does “Lightdash” MCP Server do?

The Lightdash MCP (Model Context Protocol) Server is a tool that connects AI assistants with Lightdash, a modern business intelligence (BI) and analytics platform. By providing MCP-compatible access to Lightdash’s API, this server enables AI agents and development tools to interact programmatically with Lightdash data. This integration allows developers to perform tasks such as listing projects, retrieving project details, and exploring analytics spaces and charts directly from their AI workflows. As a result, the Lightdash MCP Server enhances development productivity by simplifying data access, automating analytics-related actions, and supporting more intelligent, context-aware AI-driven processes within engineering and business intelligence workflows.

List of Prompts

No prompt templates are mentioned in the repository or documentation.

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List of Resources

No explicit MCP resource definitions are provided in the repository or documentation.

List of Tools

• list_projects: Lists all projects in the Lightdash organization, allowing users to see available analytics projects.
• get_project: Retrieves details of a specific project, providing in-depth information useful for data exploration and management.
• list_spaces: Lists all spaces within a given project, helping users navigate the organizational structure of dashboards and analytics.
• list_charts: Lists all charts in a project, enabling quick discovery and access to visualizations and dashboards.

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Use Cases of this MCP Server

• Business Intelligence Automation: Developers and AI agents can automatically retrieve lists of analytics projects, spaces, and charts, streamlining reporting and data discovery tasks.
• Data Catalog Integration: Enables the creation of automated data catalogs by exposing Lightdash project, space, and chart metadata for indexing or documentation purposes.
• AI-powered BI Assistants: Empowers AI assistants to answer questions about available analytics resources, locate dashboards, or fetch chart information without manual lookup.
• Workflow Automation: Supports automated workflows where the status of Lightdash projects or charts can trigger further actions or notifications.
• Data Exploration for Developers: Allows engineers to programmatically explore organizational analytics resources during application development, integration, or testing.

How to set it up

Windsurf

1. Ensure Node.js is installed on your system.
2. Open your Windsurf configuration file (e.g., windsurf.json).
3. Add the Lightdash MCP Server to your mcpServers section:

   {
     "mcpServers": {
       "lightdash": {
         "command": "npx",
         "args": ["lightdash-mcp-server"]
       }
     }
   }
   

4. Save your configuration and restart Windsurf.
5. Verify that the Lightdash MCP Server is active and accessible.

Securing API Keys: Store your Lightdash API keys in environment variables:

{
  "command": "npx",
  "args": ["lightdash-mcp-server"],
  "env": {
    "LIGHTDASH_API_KEY": "your_api_key"
  }
}

Claude

1. Install Node.js if not already installed.
2. Locate the Claude MCP configuration file.
3. Add Lightdash MCP Server:

   {
     "mcpServers": {
       "lightdash": {
         "command": "npx",
         "args": ["lightdash-mcp-server"]
       }
     }
   }
   

4. Save and restart Claude.
5. Ensure connectivity to the Lightdash MCP Server.

Securing API Keys:

{
  "env": {
    "LIGHTDASH_API_KEY": "your_api_key"
  }
}

Cursor

1. Install Node.js as a prerequisite.
2. Edit your Cursor configuration file.
3. Within mcpServers, add:

   {
     "mcpServers": {
       "lightdash": {
         "command": "npx",
         "args": ["lightdash-mcp-server"]
       }
     }
   }
   

4. Save changes and restart Cursor.
5. Confirm that the MCP server is running.

Securing API Keys:

{
  "env": {
    "LIGHTDASH_API_KEY": "your_api_key"
  }
}

Cline

1. Make sure Node.js is set up on your machine.
2. Open the Cline MCP servers configuration.
3. Add the Lightdash MCP Server using:

   {
     "mcpServers": {
       "lightdash": {
         "command": "npx",
         "args": ["lightdash-mcp-server"]
       }
     }
   }
   

4. Save your configuration and restart Cline.
5. Check that the MCP server is available.

Securing API Keys:

{
  "env": {
    "LIGHTDASH_API_KEY": "your_api_key"
  }
}

How to use this MCP inside flows

Using MCP in FlowHunt

To integrate MCP servers into your FlowHunt workflow, start by adding the MCP component to your flow and connecting it to your AI agent:

Click on the MCP component to open the configuration panel. In the system MCP configuration section, insert your MCP server details using this JSON format:

{
  "lightdash": {
    "transport": "streamable_http",
    "url": "https://yourmcpserver.example/pathtothemcp/url"
  }
}

Once configured, the AI agent is now able to use this MCP as a tool with access to all its functions and capabilities. Remember to change “lightdash” to whatever the actual name of your MCP server is and replace the URL with your own MCP server URL.

Overview

Based on the above tables, the Lightdash MCP Server provides essential tool integration for Lightdash analytics but lacks prompt templates, explicit resources, or sampling/roots support. It is well-documented for setup and provides clear examples of securing credentials. I would rate this MCP server a 5/10 for completeness and utility in the current state.

MCP Score