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.

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:

| Variable | Required | Description | |----------|----------|-------------| | SUPERSET_URL | Yes | Base URL of Superset (e.g. https://superset.yourcompany.com) | | Option A – Session cookie (browser / Google login, no JWT) | | | | SUPERSET_SESSION_COOKIE | Yes | Cookie string, e.g. session=<value>. Get from DevTools -> Application -> Cookies -> your Superset URL -> copy session value. See GET_TOKEN.md. | | Option B – Access token | | | | SUPERSET_ACCESS_TOKEN | Yes | JWT from browser. Optional: SUPERSET_REFRESH_TOKEN. | | Option C – Username/password | | | | SUPERSET_USERNAME | Yes | API user (e.g. admin) | | SUPERSET_PASSWORD | Yes | Password for that user | | SUPERSET_AUTH_PROVIDER | No | Auth provider; default db |

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

Open Cursor Settings → Features → MCP.
Click Add new MCP server.
Choose Run a script / command (stdio).
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

| Tool | Purpose | |------|--------| | superset_list_databases | List Superset databases (e.g. Snowflake connection) | | superset_list_datasets | List datasets; optional database_id, search | | superset_get_dataset | Get dataset by id (columns, metrics) for building charts | | superset_list_dashboards | List dashboards; optional search | | superset_get_dashboard | Get dashboard by id or slug (layout, metadata, filters) | | superset_create_dashboard | Create empty dashboard; then add charts and filters | | superset_update_dashboard | Update dashboard (title, slug, published) | | superset_delete_dashboard | Delete a dashboard by id | | superset_update_dashboard_filters | Set native filters (JSON array of filter config) | | superset_add_chart_to_dashboard | Add chart to dashboard with position (x, y, width, height) | | superset_list_charts | List charts; optional search | | superset_get_chart | Get chart by id | | superset_create_chart | Create chart (dataset_id, viz_type, slice_name, params JSON) | | superset_update_chart | Update chart (slice_name, params, description) | | superset_delete_chart | Delete a chart by id | | superset_get_dashboard_charts | List charts on a dashboard |

Workflow: Snowflake views → Superset dashboard

You tell the AI what you want: e.g. “Dashboard for view X, filter by date and region, bar chart and table.”
AI uses Snowflake MCP to inspect views/tables (e.g. list_objects, run_snowflake_query).
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

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.

Superset MCP Server