Use This MCP server To

Explore Dagster data pipelines via AI agents Monitor Dagster pipeline runs in real-time Manage data assets within Dagster through LLMs Integrate LLMs with data engineering workflows Automate data pipeline status reporting Enable AI-driven data orchestration insights Bridge LLMs and Dagster for workflow automation

README

mcp-dagster: A Dagster MCP Server

The Model Context Protocol (MCP) is an open protocol that enables seamless integration between LLM applications and external data sources and tools. This repository provides an MCP server for interacting with Dagster, the data orchestration platform.

Overview

A Model Context Protocol server that enables AI agents to interact with Dagster instances, explore data pipelines, monitor runs, and manage assets. It serves as a bridge between LLMs and your data engineering workflows.

Read our launch post to learn more.

PyPI version

Components

Tools

The server implements several tools for Dagster interaction:

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

Configuration

The server connects to Dagster using these defaults:

GraphQL endpoint: http://localhost:3000/graphql
Transport: SSE (Server-Sent Events)

Quickstart

Running the Example

Start the Dagster instance with your pipeline:

uv run dagster dev -f ./examples/open-ai-agent/pipeline.py

Run the MCP server with SSE transport:

uv run examples/open-ai-agent/run_sse_mcp.py

Start the agent loop to interact with Dagster:

uv run ./examples/open-ai-agent/agent.py

Example Interactions

Once the agent is running, you can ask questions like:

"What assets are available in my Dagster instance and what do they do?"
"Can you materialize the continent_stats asset and show me the result?"
"Check the status of recent runs and provide a summary of any failures"
"Create a new monthly aggregation asset that depends on continent_stats"

The agent will use the MCP server to interact with your Dagster instance and provide answers based on your data pipelines.

mcp-server-dagster FAQ

How do I install mcp-server-dagster?

You can install it via PyPI using 'pip install mcp-server-dagster'.

What platforms does mcp-server-dagster support?

It supports any environment where Dagster runs, typically Linux and cloud platforms.

Can mcp-server-dagster monitor live pipeline runs?

Yes, it provides real-time monitoring of Dagster pipeline executions.

How does mcp-server-dagster integrate with LLMs?

It exposes Dagster data and workflows via the MCP protocol, enabling LLMs like OpenAI, Claude, and Gemini to interact with them.

Is mcp-server-dagster secure for production use?

It follows MCP principles for secure, scoped, and observable interactions, suitable for production environments.

Does mcp-server-dagster support asset management?

Yes, it allows managing data assets within Dagster through the MCP interface.

Where can I find documentation and examples?

Documentation and usage examples are available on the GitHub repository and the launch blog post linked in the project description.