Can my agent list all software-defined assets in Dagster?

Yes. Use the 'list_assets' tool. Your agent will retrieve all software-defined assets, allowing you to identify data dependencies and verify physical storage mappings within your pipelines.

How do I check the status of a specific job run?

Provide the 'run_id' to the 'get_run' tool. Your agent will fetch detailed information for that specific execution, including status (Success, Failure, In Progress) and detailed execution logs.

Can I see active sensors and schedules via the agent?

Absolutely. Use the 'list_schedules' and 'list_sensors' tools. Your agent will pull the active automation triggers, allowing you to audit which jobs are scheduled and which sensors are listening for external events.

Dagster MCP Server for LangChain 6 tools — connect in under 2 minutes

Name: Dagster
Availability: InStock
Author: Vinkius

python.langchain.com/docs/integrations/tools/mcp

Built by Vinkius GDPR 6 Tools Framework

LangChain is the leading Python framework for composable LLM applications. Connect Dagster through Vinkius and LangChain agents can call every tool natively. combine them with retrievers, memory, and output parsers for sophisticated AI pipelines.

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

Open in ChatGPT Open in Claude Open in Perplexity

Vinkius supports streamable HTTP and SSE.

python

import asyncio
from langchain_mcp_adapters.client import MultiServerMCPClient
from langchain_openai import ChatOpenAI
from langgraph.prebuilt import create_react_agent

async def main():
    # Your Vinkius token. get it at cloud.vinkius.com
    async with MultiServerMCPClient({
        "dagster": {
            "transport": "streamable_http",
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp",
        }
    }) as client:
        tools = client.get_tools()
        agent = create_react_agent(
            ChatOpenAI(model="gpt-4o"),
            tools,
        )
        response = await agent.ainvoke({
            "messages": [{
                "role": "user",
                "content": "Using Dagster, show me what tools are available.",
            }]
        })
        print(response["messages"][-1].content)

asyncio.run(main())

Fully ManagedVinkius Servers

60%Token savings

High SecurityEnterprise-grade

IAMAccess control

EU AI ActCompliant

DLPData protection

V8 IsolateSandboxed

Ed25519Audit chain

<40msKill switch

Stream every event to Splunk, Datadog, or your own webhook in real-time

* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure

About Dagster MCP Server

Connect your Dagster (Plus or open-source) instance to any AI agent and take full control of your data orchestration and asset management through natural conversation.

LangChain's ecosystem of 500+ components combines seamlessly with Dagster through native MCP adapters. Connect 6 tools via Vinkius and use ReAct agents, Plan-and-Execute strategies, or custom agent architectures. with LangSmith tracing giving full visibility into every tool call, latency, and token cost.

What you can do

Job Orchestration — List and audit all data jobs available in your Dagster server to understand active pipeline boundaries
Run Monitoring — Fetch chronological history of recent job runs and retrieve detailed status and execution logs for specific run IDs
Asset Tracking — Enumerate software-defined assets to identify data dependencies and verify physical storage mappings
Schedules & Sensors — List all configured job schedules and active sensors listening for external events to audit automation triggers
Environment Audit — Identify deployment boundaries and verify instance connectivity across Dagster Plus or self-hosted clusters

The Dagster MCP Server exposes 6 tools through the Vinkius. Connect it to LangChain in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.

How to Connect Dagster to LangChain via MCP

Follow these steps to integrate the Dagster MCP Server with LangChain.

Install dependencies

Run pip install langchain langchain-mcp-adapters langgraph langchain-openai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token

Run the agent

Save the code and run python agent.py

Explore tools

The agent discovers 6 tools from Dagster via MCP

Why Use LangChain with the Dagster MCP Server

LangChain provides unique advantages when paired with Dagster through the Model Context Protocol.

The largest ecosystem of integrations, chains, and agents. combine Dagster MCP tools with 500+ LangChain components

Agent architecture supports ReAct, Plan-and-Execute, and custom strategies with full MCP tool access at every step

LangSmith tracing gives you complete visibility into tool calls, latencies, and token usage for production debugging

Memory and conversation persistence let agents maintain context across Dagster queries for multi-turn workflows

Dagster + LangChain Use Cases

Practical scenarios where LangChain combined with the Dagster MCP Server delivers measurable value.

RAG with live data: combine Dagster tool results with vector store retrievals for answers grounded in both real-time and historical data

Autonomous research agents: LangChain agents query Dagster, synthesize findings, and generate comprehensive research reports

Multi-tool orchestration: chain Dagster tools with web scrapers, databases, and calculators in a single agent run

Production monitoring: use LangSmith to trace every Dagster tool call, measure latency, and optimize your agent's performance

Dagster MCP Tools for LangChain (6)

These 6 tools become available when you connect Dagster to LangChain via MCP:

get_run

Get run details from Dagster

list_assets

List all assets from Dagster

list_jobs

List all jobs from Dagster

list_runs

List recent runs from Dagster

list_schedules

List all schedules from Dagster

list_sensors

List all sensors from Dagster

Example Prompts for Dagster in LangChain

Ready-to-use prompts you can give your LangChain agent to start working with Dagster immediately.

"List all jobs in my Dagster deployment"

"Show me the status of the last 5 runs"

"What assets are currently defined in my project?"

Troubleshooting Dagster MCP Server with LangChain

Common issues when connecting Dagster to LangChain through the Vinkius, and how to resolve them.

MultiServerMCPClient not found

Install: pip install langchain-mcp-adapters

Dagster + LangChain FAQ

Common questions about integrating Dagster MCP Server with LangChain.

How does LangChain connect to MCP servers?

Use langchain-mcp-adapters to create an MCP client. LangChain discovers all tools and wraps them as native LangChain tools compatible with any agent type.

Which LangChain agent types work with MCP?

All agent types including ReAct, OpenAI Functions, and custom agents work with MCP tools. The tools appear as standard LangChain tools after the adapter wraps them.