Getting Started

This guide walks through getting started with Tableau MCP. The easiest way for Cloud users to get started is to use the remote hosted Tableau MCP server. To self-host Tableau MCP or run it locally, follow the guide below.

Run with npx

The quickest way to run Tableau MCP locally. Requires Node.js 18 or later — no cloning or building needed. Configure your AI tool (MCP client) with:

{
  "mcpServers": {
    "tableau": {
      "command": "npx",
      "args": ["-y", "@tableau/mcp-server@latest"],
      "env": {
        "SERVER": "https://my-tableau-server.com",
        "SITE_NAME": "my_site",
        "PAT_NAME": "my_pat",
        "PAT_VALUE": "pat_value"
      }
    }
  }
}

npx will automatically download and run the latest published version from npm.

Building From Source

Building from source is appropriate for those working on or contributing to the project, or for anyone who wants to use the latest changes in between official releases. Developers will need to have Git and Node installed.

Working with the source code

1. Clone the repository.
2. Install Node.js.
3. npm install
4. npm run build

To keep up with repo changes:

1. Pull latest changes: git pull
2. npm install
3. npm run build
4. Relaunch your AI tool or 'refresh' the MCP tools.

Run with Node

After building from source, configure your AI tool (MCP client) to use the MCP server with a snippet like this:

{
  "mcpServers": {
    "tableau": {
      "command": "node",
      "args": ["full/path/to/build/index.js"],
      "env": {
        "SERVER": "https://my-tableau-server.com",
        "SITE_NAME": "my_site",
        "PAT_NAME": "my_pat",
        "PAT_VALUE": "pat_value"
      }
    }
  }
}

The project includes a template file config.stdio.json you can use as an example.

Run with Docker

To use the Docker version of Tableau MCP, make sure that Docker is running, then build the image from source:

$ npm run build:docker
$ docker images
REPOSITORY    TAG       IMAGE ID       CREATED        SIZE
tableau-mcp   latest    c721228b6dd3   15 hours ago   260MB

Next, configure your AI tool (MCP client) to use the MCP server with a snippet like this:

{
  "mcpServers": {
    "tableau": {
      "command": "docker",
      "args": ["run", "-i", "--rm", "--env-file", "env.list", "tableau-mcp"]
    }
  }
}

The project includes a template file config.docker.json you can use as an example.

Remember to build the Docker image again whenever you pull the latest repo changes. Also you'll need to relaunch your AI tool so it starts using the updated image.

Run with Heroku

See Deploy to Heroku for new experimental Heroku support.

Troubleshooting

Here are some common issues that might come up – and how to solve them. The examples and screenshots are from Claude Desktop but can apply similarly with any AI tools.

401 Unauthorized

When the AI client is using tools through Tableau MCP, it might fail and report a "401 Unauthorized" error.

Solutions:

• Double-check that the server URL and site are correct
• For PAT_NAME, ensure you are providing the name of the PAT, not your username or email address
• Generate a new personal access token (PATs can expire after 15 days if not used)

403 Forbidden

For Tableau MCP to work, the user must have the "API access" permission enabled. In cases where the user does not have that permission, a "403 Forbidden" error can occur.

Solution:

• Grant API access to your user for that specific data source
• More likely, request that the admin or project owner do that for you