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Integrates with Redash data visualization platform to enable natural language querying, dashboard creation, and data source management for analyzing and visualizing data through conversational interfaces.

Publishersuthio
Repositoryredash-mcp
LanguageTypeScript
Forks
34
Stars
81
Available tools
0
Transport typestdio
Categories
LicenseMIT
Links
  • Connect tools to AI workflows

    Redash exposes MCP capabilities that can be used by compatible AI clients and agents.

  • 0 available tools

    Browse the callable actions below, including names and descriptions when provided by the server.

  • Ready-to-copy setup

    Use the installation snippets to configure this server in your preferred MCP client.

  • Open source signals

    81 stars and 34 forks from the linked repository.

Redash MCP Server

Model Context Protocol (MCP) server for integrating Redash with AI assistants like Claude.

Features

  • Connect to Redash instances via the Redash API
  • List available queries and dashboards as resources
  • Execute queries and retrieve results
  • Create and manage queries (create, update, archive)
  • List data sources for query creation
  • Get dashboard details and visualizations

Prerequisites

  • Node.js (v18 or later)
  • npm or yarn
  • Access to a Redash instance
  • Redash API key

Environment Variables

The server requires the following environment variables:

Optional variables:

  • REDASH_TIMEOUT: Timeout for API requests in milliseconds (default: 30000)
  • REDASH_MAX_RESULTS: Maximum number of results to return (default: 1000)
  • REDASH_EXTRA_HEADERS: Extra HTTP headers to include with every Redash request. Accepts either a JSON object string or a semicolon/comma-separated list of key=value pairs.
  • REDASH_SOCKS_PROXY: SOCKS proxy URL for routing requests through a proxy (e.g., socks5h://localhost:1080). Use socks5h:// (with h) to delegate DNS resolution to the proxy, which is required for internal hostnames that don't resolve on the local machine.

Examples:

JSON (recommended):

REDASH_EXTRA_HEADERS='{"CF-Access-Client-Id":"<client_id>","CF-Access-Client-Secret":"<client_secret>"}'

Key/value list:

REDASH_EXTRA_HEADERS=CF-Access-Client-Id=<client_id>;CF-Access-Client-Secret=<client_secret>

Notes:

  • The Authorization header is managed by the server (Key <REDASH_API_KEY>) and cannot be overridden.
  • All extra headers are added to every request made to Redash.

Installation

  1. Clone this repository:

    bash
    git clone https://github.com/suthio/redash-mcp.git
    cd redash-mcp
  2. Install dependencies:

    bash
    npm install
  3. Create a .env file with your Redash configuration:

    REDASH_URL=https://your-redash-instance.com
    REDASH_API_KEY=your_api_key
    # Optional: Cloudflare Access (or other gateway) headers
    # REDASH_EXTRA_HEADERS='{"CF-Access-Client-Id":"<client_id>","CF-Access-Client-Secret":"<client_secret>"}'
  4. Build the project:

    bash
    npm run build
  5. Start the server:

    bash
    npm start

Usage with Claude for Desktop

To use this MCP server with Claude for Desktop, configure it in your Claude for Desktop configuration file:

macOS: ~/Library/Application Support/Claude/claude_desktop_config.json Windows: %APPDATA%\Claude\claude_desktop_config.json

Add the following configuration (edit paths as needed):

json
{
  "mcpServers": {
    "redash": {
      "command": "npx",
      "args": [
         "-y",
         "@suthio/redash-mcp"
      ],
      "env": {
        "REDASH_API_KEY": "your-api-key",
        "REDASH_URL": "https://your-redash-instance.com"
      }
    }
  }
}

Available Tools

Query Management

  • list-queries: List all available queries in Redash
  • get-query: Get details of a specific query
  • create-query: Create a new query in Redash
  • update-query: Update an existing query in Redash
  • archive-query: Archive (soft-delete) a query
  • list-data-sources: List all available data sources

Query Execution

  • execute-query: Execute a query and return results
  • execute-adhoc-query: Execute an ad-hoc query without saving it to Redash
  • get-query-results-csv: Get query results in CSV format (supports optional refresh for latest data)

Dashboard Management

  • list-dashboards: List all available dashboards
  • get-dashboard: Get dashboard details and visualizations
  • get-visualization: Get details of a specific visualization

Visualization Management

  • create-visualization: Create a new visualization for a query
  • update-visualization: Update an existing visualization
  • delete-visualization: Delete a visualization

Development

Run in development mode:

bash
npm run dev

Testing

Unit Tests

bash
npm test

E2E Tests

bash
npm run e2e:test

E2E tests use these default values (can be overridden with environment variables):

Override example:

bash
REDASH_URL=https://your-instance.com REDASH_API_KEY=your_key npm run e2e:test

Manual Testing

bash
npm run inspector

Version History

  • v1.1.0: Added query management functionality (create, update, archive)
  • v1.0.0: Initial release

License

MIT

Installation

TypingMind
Prerequisites:

Node.js 18+

{
  "mcpServers": {
    "redash": {
      "command": "npx",
      "args": [
        "-y",
        "@suthio/redash-mcp"
      ],
      "env": {
        "REDASH_API_KEY": "your-api-key",
        "REDASH_URL": "https://your-redash-instance.com"
      }
    }
  }
}

Use Redash MCP with multiple AI models

TypingMind connects MCP tools at the workspace level, so once Redash is connected, you can use it with different AI models in TypingMind instead of setting it up separately for each model. This MCP runs locally through the TypingMind MCP connector on your device.

Setup guide to use the local connector

Use this when the MCP server needs access to local files, apps, or private resources on your computer.

1

Open the MCP settings

In TypingMind, go to Settings, Advanced Settings, then Model Context Protocol and choose Setup Connector.

  1. Open TypingMind in your browser.
  2. Click the Settings icon.
  3. Go to Advanced Settings.
  4. Open the Model Context Protocol section.
  5. Click Setup Connector and choose This Device.
TypingMind MCP connector setup screen with This Device selected
2

Run the connector command

Choose This Device, copy the command from TypingMind, and run it in Terminal. Keep the process running while you use MCP.

  1. Copy the setup command shown by TypingMind.
  2. Open Terminal on macOS or Windows Terminal on Windows.
  3. Paste and run the command.
  4. Approve the package install if Terminal asks you to proceed.
  5. Keep the Terminal window running while using MCP tools.
3

Add Redash as a server

When the connector status is Ready, click Edit Servers and paste the MCP server configuration.

  1. Wait until the connector status shows Ready.
  2. Click Edit Servers.
  3. Paste the Redash MCP server configuration.
  4. Save the server list.
  5. Refresh if you want to confirm the connector is still ready.
TypingMind MCP settings showing active server and Edit Servers button
{
  "mcpServers": {
    "redash": {
      "command": "npx",
      "args": [
        "-y",
        "redash-mcp"
      ]
    }
  }
}
4

Use it across models

Save the server list, open Plugins, enable the Redash MCP tools, then select any supported AI model in TypingMind and use the tools in chat or assign them to an AI agent.

  1. Open the Plugins page in TypingMind.
  2. Enable the Redash MCP tools.
  3. Start a chat and choose the AI model you want to use.
  4. Use the MCP tools in chat or assign them to an AI agent.
  5. Switch to another AI model whenever needed without reconnecting MCP.
TypingMind chat using enabled MCP tools with a selected AI model
Can you use Redash to help me with this task?
Redash
Sure. I read it.
Here is what I found using Redash.

Frequently asked questions

What is the Redash MCP server used for?

Redash is an MCP server that lets compatible AI clients connect to external tools and context. In TypingMind, you can add this MCP server once and make its tools available in your AI workspace.

Can I use Redash MCP with multiple AI models in TypingMind?

Yes. TypingMind connects MCP tools at the workspace level, so you can use Redash with different AI models such as Claude, ChatGPT, Gemini, or other models you have configured in TypingMind without setting up the MCP server separately for each model.

Why use Redash MCP with TypingMind?

TypingMind is one of the best frontends for LLM chat because it brings multiple AI models, prompts, plugins, AI agents, API keys, and MCP tools into one workspace. With Redash connected, you can use its MCP tools across your preferred models while keeping your chat workflow organized in TypingMind.

How do I connect Redash MCP to TypingMind?

Redash runs through the TypingMind local MCP connector. This is best when the MCP server needs access to local files, desktop apps, command-line tools, or private resources on your computer.

What tools does Redash MCP provide in TypingMind?

Redash exposes MCP capabilities that can be enabled from the TypingMind Plugins page and used in chat or assigned to AI agents.

Do I need to share my API keys with TypingMind to use Redash MCP?

No. TypingMind is local-first and lets you keep your model providers, API keys, prompts, and MCP configuration under your control. If Redash requires authentication, add the required headers, OAuth settings, or local configuration for that MCP server when you create the connection.

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