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 CrewAI 6 tools — connect in under 2 minutes

Name: Dagster
Availability: InStock
Author: Vinkius

docs.crewai.com/mcp/overview

Built by Vinkius GDPR 6 Tools Framework

Connect your CrewAI agents to Dagster through Vinkius, pass the Edge URL in the `mcps` parameter and every Dagster tool is auto-discovered at runtime. No credentials to manage, no infrastructure to maintain.

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

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Vinkius supports streamable HTTP and SSE.

python

from crewai import Agent, Task, Crew

agent = Agent(
    role="Dagster Specialist",
    goal="Help users interact with Dagster effectively",
    backstory=(
        "You are an expert at leveraging Dagster tools "
        "for automation and data analysis."
    ),
    # Your Vinkius token. get it at cloud.vinkius.com
    mcps=["https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"],
)

task = Task(
    description=(
        "Explore all available tools in Dagster "
        "and summarize their capabilities."
    ),
    agent=agent,
    expected_output=(
        "A detailed summary of 6 available tools "
        "and what they can do."
    ),
)

crew = Crew(agents=[agent], tasks=[task])
result = crew.kickoff()
print(result)

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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.

When paired with CrewAI, Dagster becomes a first-class tool in your multi-agent workflows. Each agent in the crew can call Dagster tools autonomously, one agent queries data, another analyzes results, a third compiles reports, all orchestrated through Vinkius with zero configuration overhead.

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 CrewAI 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 CrewAI via MCP

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

Install CrewAI

Run pip install crewai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token from cloud.vinkius.com

Customize the agent

Adjust the role, goal, and backstory to fit your use case

Run the crew

Run python crew.py. CrewAI auto-discovers 6 tools from Dagster

Why Use CrewAI with the Dagster MCP Server

CrewAI Multi-Agent Orchestration Framework provides unique advantages when paired with Dagster through the Model Context Protocol.

Multi-agent collaboration lets you decompose complex workflows into specialized roles, one agent researches, another analyzes, a third generates reports, each with access to MCP tools

CrewAI's native MCP integration requires zero adapter code: pass Vinkius Edge URL directly in the `mcps` parameter and agents auto-discover every available tool at runtime

Built-in task delegation and shared memory mean agents can pass context between steps without manual state management, enabling multi-hop reasoning across tool calls

Sequential and hierarchical crew patterns map naturally to real-world workflows: enumerate subdomains → analyze DNS history → check WHOIS records → compile findings into actionable reports

Dagster + CrewAI Use Cases

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

Automated multi-step research: a reconnaissance agent queries Dagster for raw data, then a second analyst agent cross-references findings and flags anomalies. all without human handoff

Scheduled intelligence reports: set up a crew that periodically queries Dagster, analyzes trends over time, and generates executive briefings in markdown or PDF format

Multi-source enrichment pipelines: chain Dagster tools with other MCP servers in the same crew, letting agents correlate data across multiple providers in a single workflow

Compliance and audit automation: a compliance agent queries Dagster against predefined policy rules, generates deviation reports, and routes findings to the appropriate team

Dagster MCP Tools for CrewAI (6)

These 6 tools become available when you connect Dagster to CrewAI 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 CrewAI

Ready-to-use prompts you can give your CrewAI 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 CrewAI

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

MCP tools not discovered

Ensure the Edge URL is correct. CrewAI connects lazily when the crew starts. check console output.

Agent not using tools

Make the task description specific. Instead of "do something", say "Use the available tools to list contacts".

Timeout errors

CrewAI has a 10s connection timeout by default. Ensure your network can reach the Edge URL.

Rate limiting or 429 errors

Vinkius enforces per-token rate limits. Check your subscription tier and request quota in the dashboard. Upgrade if you need higher throughput.

Dagster + CrewAI FAQ

Common questions about integrating Dagster MCP Server with CrewAI.

How does CrewAI discover and connect to MCP tools?

CrewAI connects to MCP servers lazily. when the crew starts, each agent resolves its MCP URLs and fetches the tool catalog via the standard tools/list method. This means tools are always fresh and reflect the server's current capabilities. No tool schemas need to be hardcoded.

Can different agents in the same crew use different MCP servers?

Yes. Each agent has its own mcps list, so you can assign specific servers to specific roles. For example, a reconnaissance agent might use a domain intelligence server while an analysis agent uses a vulnerability database server.

What happens when an MCP tool call fails during a crew run?

CrewAI wraps tool failures as context for the agent. The LLM receives the error message and can decide to retry with different parameters, fall back to a different tool, or mark the task as partially complete. This resilience is critical for production workflows.

Can CrewAI agents call multiple MCP tools in parallel?

CrewAI agents execute tool calls sequentially within a single reasoning step. However, you can run multiple agents in parallel using process=Process.parallel, each calling different MCP tools concurrently. This is ideal for workflows where separate data sources need to be queried simultaneously.

Can I run CrewAI crews on a schedule (cron)?

Yes. CrewAI crews are standard Python scripts, so you can invoke them via cron, Airflow, Celery, or any task scheduler. The crew.kickoff() method runs synchronously by default, making it straightforward to integrate into existing pipelines.