Dagster MCP

An MCP server that gives AI agents full visibility and control over your Dagster instance — like an SRE for your data pipelines.

Works with any MCP client: Claude Code, Claude Desktop, Cursor, and more.

Why this exists

Data pipelines break at 3 AM. Schedules silently stop firing. Assets go stale. Instead of waking up to a dashboard full of red, give your AI agent the tools to monitor, diagnose, and fix your Dagster instance autonomously.

Agent: Checking instance health...
       get_instance_status() -> healthy: false, daemon "SCHEDULER" unhealthy

Agent: Scheduler daemon is down. Let me check recent failures...
       get_runs(statuses=["FAILURE"], limit=5) -> 3 failed runs in the last hour

Agent: Diagnosing the most recent failure...
       get_run_failure_summary("run_abc123") ->
         failed_steps: ["transform_orders"]
         root_cause: "NullPointerError: column 'price' is null"
         suggestions: ["Single step failed — consider re-running from failure"]

Agent: Re-launching the failed job...
       launch_job("etl_pipeline", "my_project") -> run_id: "run_def456", status: STARTED

What it does

23 tools across 6 categories, designed for autonomous DataOps workflows:

Actions are opt-in: set DAGSTER_READ_ONLY=false to enable write operations.

Quick start

Prerequisites

• Python 3.12+
• uv (recommended) or pip
• A running Dagster instance (self-hosted or Cloud)

Install

The package is published on PyPI.

Option A — run directly with uvx (no install needed):

uvx dagster-mcp

Option B — install with pip:

pip install dagster-mcp

Option C — clone and run:

git clone https://github.com/fabdendev/dagster-mcp.git
cd dagster-mcp
uv sync

Configure

Single environment

Self-hosted:

export DAGSTER_URL=http://localhost:3000

Dagster Cloud:

export DAGSTER_URL=https://myorg.dagster.cloud/prod
export DAGSTER_API_TOKEN=your-dagster-cloud-user-token

Custom auth / proxy headers:

export DAGSTER_EXTRA_HEADERS='{"Authorization":"Bearer your-token","X-My-Header":"value"}'

Multiple environments

Use DAGSTER_ENVS to configure several Dagster instances in one server. Every tool then accepts an optional env parameter so the LLM can target the right instance.

export DAGSTER_ENVS='{
  "prod": {"url": "https://myorg.dagster.cloud/prod", "token": "prod-token"},
  "staging": {"url": "https://myorg.dagster.cloud/staging", "token": "stg-token"},
  "dev": {"url": "http://localhost:3000"}
}'
export DAGSTER_DEFAULT_ENV=prod

When DAGSTER_ENVS is set, DAGSTER_URL / DAGSTER_API_TOKEN / DAGSTER_EXTRA_HEADERS are ignored. If only one env is defined, it is used automatically even without DAGSTER_DEFAULT_ENV.

Add to your MCP client

{
  "mcpServers": {
    "dagster": {
      "command": "uvx",
      "args": ["dagster-mcp"],
      "env": {
        "DAGSTER_URL": "http://localhost:3000"
      }
    }
  }
}

{
  "mcpServers": {
    "dagster": {
      "command": "uvx",
      "args": ["dagster-mcp"],
      "env": {
        "DAGSTER_ENVS": "{\"prod\":{\"url\":\"https://myorg.dagster.cloud/prod\",\"token\":\"prod-token\"},\"dev\":{\"url\":\"http://localhost:3000\"}}",
        "DAGSTER_DEFAULT_ENV": "prod"
      }
    }
  }
}

{
  "mcpServers": {
    "dagster": {
      "command": "uvx",
      "args": ["dagster-mcp"],
      "env": {
        "DAGSTER_URL": "http://localhost:3000"
      }
    }
  }
}

{
  "mcpServers": {
    "dagster": {
      "command": "uv",
      "args": ["run", "--directory", "/path/to/dagster-mcp", "dagster-mcp"],
      "env": {
        "DAGSTER_URL": "http://localhost:3000"
      }
    }
  }
}

Tool reference

Runs

Assets

Two-step asset workflow

Start by resolving and reviewing a selection expression:

resolve_asset_selection(
  asset_selection="group:analytics and (kind:dbt or key:*benchmark)"
)

→ {
    "asset_keys": [
      "warehouse/analytics/orders",
      "warehouse/analytics/reranker_benchmark"
    ],
    "assets": [...]
  }

For concrete, unpartitioned assets, pass the returned keys to materialize_assets with any required launch config and tags:

materialize_assets(
  asset_keys=[
    "warehouse/analytics/orders",
    "warehouse/analytics/reranker_benchmark"
  ],
  run_config={"ops": {"benchmark": {"config": {"limit": 1000}}}},
  tags={"triggered_by": "agent"}
)

For partitioned assets, pass the same resolved keys to backfill_assets instead:

backfill_assets(
  asset_keys=["warehouse/analytics/daily_orders"],
  partition_start="2026-07-01",
  partition_end="2026-07-07",
  run_config={"resources": {"warehouse": {"config": {"pool": "benchmark"}}}}
)

resolve_asset_selection is available in both read-only and read-write modes. It returns external, observable, non-executable, and partitioned matches so the caller can inspect the complete result. The write tools re-fetch current asset definitions before execution. resolve_asset_selection and materialize_assets require Dagster 1.9+.

Jobs, Schedules & Sensors

Instance & Code Locations

Write Operations

Write tools require DAGSTER_READ_ONLY=false (default is true).

How it differs from the official Dagster MCP

They serve different purposes and work well together.

Compatibility

The monitoring and existing action tools are tested with Dagster 1.6+. resolve_asset_selection and materialize_assets require Dagster 1.9+ and verify the required GraphQL capabilities before querying or launching. The RunsFilter field name (jobName vs pipelineName) is auto-detected via schema introspection. Configured asset backfills also feature-detect LaunchBackfillParams.runConfigData and return a clear compatibility error when an older schema does not expose it.

Development

uv sync --extra dev
uv run ruff check dagster_mcp/    # lint
uv run pytest                     # run the test suite
uv run python -m dagster_mcp      # start server locally

License

MIT