mcp-dagster: A Dagster MCP Server

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Basic information

About mcp-dagster: A Dagster MCP Server

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#mcp-server#dagster#data-orchestration

mcp-dagster: A Dagster MCP Server config

Add this server to your MCP-compatible client using the configuration below.

{
  "mcpServers": {
    "mcp-server-dagster": {
      "command": "uv",
      "args": [
        "run",
        "dagster",
        "dev",
        "-f",
        "./examples/open-ai-agent/pipeline.py"
      ]
    }
  }
}

mcp-dagster: A Dagster MCP Server tools

Lists all available Dagster repositories

Lists all jobs in a specific repository

Lists all assets in a specific repository

Gets recent Dagster runs (default limit: 10)

Gets detailed information about a specific run

Launches a Dagster job run

Materializes a specific Dagster asset

Terminates an in-progress Dagster run

Gets detailed information about a specific asset

Overview

What is mcp-dagster?

mcp-dagster is a Model Context Protocol server that enables AI agents to interact with Dagster, the data orchestration platform. It bridges LLMs and data engineering workflows by allowing agents to explore pipelines, monitor runs, and manage assets.

How to use mcp-dagster?

Install the server from PyPI (mcp-server-dagster) and run it with uv. Start a Dagster instance with your pipeline, then launch the MCP server using SSE transport (uv run examples/open-ai-agent/run_sse_mcp.py). Finally, start the agent loop with uv run ./examples/open-ai-agent/agent.py. The server connects to http://localhost:3000/graphql by default.

Key features of mcp-dagster

list_repositories – Lists all available Dagster repositories
list_jobs – Lists all jobs in a specific repository
list_assets – Lists all assets in a specific repository
recent_runs – Gets recent Dagster runs (default limit: 10)
get_run_info – Gets detailed information about a specific run
launch_run – Launches a Dagster job run
materialize_asset – Materializes a specific Dagster asset
terminate_run – Terminates an in-progress Dagster run
get_asset_info – Gets detailed information about a specific asset

Use cases of mcp-dagster

Explore available assets and their descriptions in a Dagster instance
Check the status of recent runs and summarize failures
Materialize a specific asset on demand via natural language
Launch or terminate Dagster job runs programmatically
Create new assets that depend on existing ones

Frequently asked questions

What is the default GraphQL endpoint?

The default endpoint is `http://localhost:3000/graphql`.

What transport does the server use?

It uses SSE (Server-Sent Events) transport.

How can I install mcp-dagster?

It is available on PyPI as `mcp-server-dagster` and can be run with `uv`.

What is the default limit for recent runs?

The default limit for `recent_runs` is 10 runs.

How do I start interacting with Dagster through mcp-dagster?

Start a Dagster instance, run the MCP server with SSE transport, then start the agent loop as shown in the quickstart.

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