Superset MCP Server

MCP server that exposes Apache Superset as tools so the AI can build dashboards from Cursor: create dashboards, add charts from datasets (e.g. Snowflake views), and set filters from your instructions.

Sharing with the team

Best approach: Each colleague does a one-time setup on their machine with their own Superset credentials. No credentials are stored in the repo.

• Each person: Follows TEAM_SETUP.md once: clone → pip install -e . → get their own auth (see GET_TOKEN.md) → add Superset MCP in Cursor with their path and credentials → reload MCP.

Related MCP server: metabase-mcp

Prerequisites

• Python 3.10+

• A running Superset instance with API enabled

• In Superset: at least one database (e.g. Snowflake) and datasets (tables/views) that you want to use in dashboards

Setup

1. Install dependencies

From the directory that contains mcp_superset (the folder can live anywhere):

cd <path-to-mcp_superset>
pip install -e .
# or with uv:
uv pip install -e .

2. Environment variables

Use one of: (A) session cookie, (B) access token, or (C) username/password. Set in Cursor MCP config or your shell:

Session cookie (when you only have cookie, no Authorization header):
See GET_TOKEN.md: log in to Superset, F12 -> Application -> Cookies -> your Superset URL -> copy the session cookie value, then set SUPERSET_SESSION_COOKIE=session=<paste value>. Session expires when you close the browser or after some time; get a fresh cookie when you get 401s.

Do not commit credentials. Use Cursor’s MCP env or a local .env that is gitignored.

3. Add the server in Cursor

1. Open Cursor Settings → Features → MCP.

2. Click Add new MCP server.

3. Choose Run a script / command (stdio).

4. Configure:

Option A – Use your Python (recommended)

• Command:
  python (or the full path to your Python / venv, e.g. <path-to-mcp_superset>\.venv\Scripts\python.exe)

• Arguments:
  -m mcp_superset.server

• Working directory:
  Full path to the mcp_superset folder (e.g. c:\Bio\cursor_projects\mcp_superset)

• Env (add here or in Cursor MCP env):
  SUPERSET_URL, SUPERSET_USERNAME, SUPERSET_PASSWORD

Option B – Global install

If you installed the package globally:

• Command:
  mcp-server-superset

• Env: same as above.

Option C – JSON config (Cursor MCP)

If your Cursor MCP is configured via JSON, add something like:

{
  "mcpServers": {
    "superset": {
      "command": "python",
      "args": ["-m", "mcp_superset.server"],
      "cwd": "C:\\path\\to\\mcp_superset",
      "env": {
        "SUPERSET_URL": "https://superset.yourcompany.com",
        "SUPERSET_USERNAME": "your_user",
        "SUPERSET_PASSWORD": "your_password"
      }
    }
  }
}

Replace C:\\path\\to\\mcp_superset with the actual path where you placed the folder.

Restart Cursor or reload MCP after adding the server.

Tools exposed to the AI

Workflow: Snowflake views → Superset dashboard

1. You tell the AI what you want: e.g. “Dashboard for view X, filter by date and region, bar chart and table.”

2. AI uses Snowflake MCP to inspect views/tables (e.g. list_objects, run_snowflake_query).

3. AI uses Superset MCP to:

   • superset_list_datasets to find the dataset that points at that view

   • superset_get_dataset to see columns

   • superset_create_dashboard and then superset_create_chart for each chart

   • superset_add_chart_to_dashboard to place them

   • superset_update_dashboard_filters to add the filters you asked for

4. You open the dashboard in Superset and refine if needed.

Native filters (dashboard filters)

superset_update_dashboard_filters takes a JSON string that is an array of filter objects. Each object typically has:

• id: unique string id for the filter

• name: label shown in the UI

• filterType: e.g. filter_select, filter_time, filter_timegrain

• targets: which charts/columns the filter applies to

• defaultDataMask: default value

• scope: scope of the filter

The AI can build this from your instructions (e.g. “add a date range and a region dropdown”) by following Superset’s native filter schema.

Chart params

For superset_create_chart, params is a JSON string object. Contents depend on viz_type, for example:

• table: metrics, groupby, order_desc, row_limit, etc.

• big_number: metric, compare_lag, etc.

• line / bar: metrics, groupby, time_range, order_desc, etc.

The AI should use superset_get_dataset to see available columns/metrics and build valid params.

Run the server locally (optional)

cd mcp_superset
set SUPERSET_URL=https://...
set SUPERSET_USERNAME=admin
set SUPERSET_PASSWORD=...
python -m mcp_superset.server

The server uses stdio; Cursor will start it automatically when the tools are used.