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mcp-dagster: A Dagster MCP Server

@kyryl-opens-ml

リポジトリ

基本情報

カテゴリ

その他

ライセンス

Apache-2.0

トランスポート

stdio

作者

kyryl-opens-ml

mcp-dagster: A Dagster MCP Server について

概要はまだありません

#mcp-server#dagster#data-orchestration

mcp-dagster: A Dagster MCP Server の設定

以下の設定を使って、このサーバーを MCP 対応クライアントに追加してください。

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

mcp-dagster: A Dagster MCP Server のツール

9

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

概要

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

よくある質問

1

What is the default GraphQL endpoint?

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

2

What transport does the server use?

It uses SSE (Server-Sent Events) transport.

3

How can I install mcp-dagster?

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

4

What is the default limit for recent runs?

The default limit for `recent_runs` is 10 runs.

5

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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