mcp-dagster: A Dagster MCP Server
@kyryl-opens-ml
mcp-dagster: A Dagster MCP Server について
概要はまだありません
基本情報
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"
]
}
}
}ツール
9Lists 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 repositorieslist_jobs– Lists all jobs in a specific repositorylist_assets– Lists all assets in a specific repositoryrecent_runs– Gets recent Dagster runs (default limit: 10)get_run_info– Gets detailed information about a specific runlaunch_run– Launches a Dagster job runmaterialize_asset– Materializes a specific Dagster assetterminate_run– Terminates an in-progress Dagster runget_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
FAQ from mcp-dagster
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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