An MCP server that gives AI agents full visibility and control over your Dagster instance — like an SRE for your data pipelines.
Dagster Mcp is an MCP project maintained in the fabdendev/dagster-mcp repository. The maintainer describes its purpose as “An MCP server that gives AI agents full visibility and control over your Dagster instance — like an SRE for your data pipelines”. Read the project documentation for the complete scope.
The documented pypi package is dagster-mcp. The configuration below comes from the maintainer’s example; adapt its paths and account values to your environment.
The example declares DAGSTER_URL. Secret-looking values have been replaced with placeholders. Required and optional labels are shown only when the documentation states them.
Project documentation · Repository · Discovery listing. This profile records the public documentation checked on 2026-10-08.
Check the linked publisher documentation and your connected client's tool list. Names and availability can vary with the server version and account permissions.
| Tool | What it does |
|---|---|
| get_runs | List recent runs, filter by job name and/or status |
| get_run_status | Get status, config, tags, and run lineage (re-execution chain via rootRunId/parentRunId) |
| get_run_logs | Get structured log events with pagination and optional level filtering (ERROR, WARNING, INFO); EngineEvent events include metadataEntries |
| get_run_stats | Get per-step execution stats: timing, materializations, expectation results |
| get_run_failure_summary | Consolidated failure diagnosis — failed steps, root cause error, step durations, and suggestions in one call |
| search_assets | Discover assets by key prefix or group name |
| resolve_asset_selection | Resolve key/group/tag/kind/owner predicates, wildcards, boolean logic, roots/sinks, and lineage traversal into concrete asset keys without launching anything |
| get_asset_details | Get description, upstream/downstream dependencies, partitions, latest materialization |
| get_recent_materializations | Get materialization history with metadata for an asset |
| get_asset_health | Consolidated health view — staleness, freshness policy, last run status (works with single asset or entire group) |
| list_jobs | List jobs across code locations, optionally filtered by exact repository_name and/or location_name (use to find names for launch_job) |
| list_schedules | List schedules with status (RUNNING/STOPPED), cron, target job, next tick |
| list_sensors | List sensors with status and target jobs |
| get_tick_history | Tick-by-tick history for a schedule or sensor — essential for detecting silent failures. Accepts optional repository_name / location_name to disambiguate a… |
| get_instance_status | Start here — global health: daemon status, queued run count, code location errors |
| list_code_locations | List all code locations and their load status |
| list_backfills | List recent backfills with status and partition progress |
| materialize_assets | Launch concrete, unpartitioned asset keys with run config and tags; infers one compatible repository/job, includes compatible checks, and expands required… |
| backfill_assets | Launch a partition backfill by asset selection with optional run config; respects each asset's BackfillPolicy server-side |
| launch_job | Launch a named job; asset_keys remains supported for compatibility and is sent through GraphQL assetSelection, but the two-step asset workflow is preferred |
| launch_job_with_partitions | Launch a partitioned job for one or more partition keys; creates a backfill (supports from_failure to retry only failed steps) |
| start_schedule | Start (enable) a schedule so it launches runs on its cron |
| stop_schedule | Stop (disable) a schedule — persists across restarts; does not terminate in-flight runs |
| start_sensor | Start (enable) a sensor so it resumes evaluating |
| stop_sensor | Stop (disable) a sensor — the fix for a runaway or erroring sensor found via get_tick_history |
| terminate_run | Stop a stuck or runaway run |
| reload_code_location | Reload a code location after deploy |
{
"mcpServers": {
"dagster": {
"command": "uvx",
"args": [
"dagster-mcp"
],
"env": {
"DAGSTER_URL": "http://localhost:3000"
}
}
}
}From the project documentation. Replace placeholders and example paths before connecting.
| Variable | Description | Required |
|---|---|---|
| DAGSTER_URL | Declared in the maintainer’s client configuration. | Check documentation |
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