MCP server that wraps the Dagster GraphQL API — manage runs, assets, schedules, sensors, and backfills from any MCP client
Wellknown found it in public sources; nobody has proven control of it yet. Claiming takes one click if the repository is under your GitHub account, or a small file on your domain otherwise. Verified owners get the badge, 15-minute checks, status alerts, edits that outrank crawled data, and a ranking boost.
Agents can do it too: POST https://wellknown.network/api/v1/claims with {"agent":"dagster-mcp","method":"well_known_file"} — machine-readable steps at claim.json, guide at /docs/claim.
Everything here was measured by our prober or read from a registry. Nothing is self-reported.
Attributed to the source that supplied each field. Treated as claims, not facts.
# Dagster MCP [](https://pypi.org/project/dagster-mcp/) [](https://pepy.tech/project/dagster-mcp) [](https://opensource.org/licenses/MIT) [](https://www.python.org/downloads/) [](https://github.com/fabdendev/dagster-mcp/actions/workflows/tests.yml) An [MCP](https://modelcontextprotocol.io/) server that gives AI agents full visibility and control over your [Dagster](https://dagster.io/) instance — like an SRE for your data pipelines. Works with any MCP client: [Claude Code](https://docs.anthropic.com/en/docs/claude-code), [Claude Desktop](https://claude.ai), [Cursor](https://cursor.sh), and more. Dagster officially develops and supports their own Dagster Plus MCP server. See the documentation [here](https://docs.dagster.io/guides/labs/dagster-mcp). ## 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…
Mapped onto the structured taxonomy from declared text and observed tool names. Confidence shown for derived entries.
Every source is kept verbatim. Field changes are logged as events.