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 Pydantic AI 6 tools — connect in under 2 minutes

Name: Dagster
Availability: InStock
Author: Vinkius

ai.pydantic.dev/mcp

Built by Vinkius GDPR 6 Tools SDK

Pydantic AI brings type-safe agent development to Python with first-class MCP support. Connect Dagster through the Vinkius and every tool is automatically validated against Pydantic schemas — catch errors at build time, not in production.

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 pydantic_ai import Agent
from pydantic_ai.mcp import MCPServerHTTP

async def main():
    # Your Vinkius token — get it at cloud.vinkius.com
    server = MCPServerHTTP(url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")

    agent = Agent(
        model="openai:gpt-4o",
        mcp_servers=[server],
        system_prompt=(
            "You are an assistant with access to Dagster "
            "(6 tools)."
        ),
    )

    result = await agent.run(
        "What tools are available in Dagster?"
    )
    print(result.data)

asyncio.run(main())

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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.

Pydantic AI validates every Dagster tool response against typed schemas, catching data inconsistencies at build time. Connect 6 tools through the Vinkius and switch between OpenAI, Anthropic, or Gemini without changing your integration code — full type safety, structured output guarantees, and dependency injection for testable agents.

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 Pydantic AI 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 Pydantic AI via MCP

Follow these steps to integrate the Dagster MCP Server with Pydantic AI.

Install Pydantic AI

Run pip install pydantic-ai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token

Run the agent

Save to agent.py and run: python agent.py

Explore tools

The agent discovers 6 tools from Dagster with type-safe schemas

Why Use Pydantic AI with the Dagster MCP Server

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

Full type safety: every MCP tool response is validated against Pydantic models, catching data inconsistencies before they reach your application

Model-agnostic architecture — switch between OpenAI, Anthropic, or Gemini without changing your Dagster integration code

Structured output guarantee: Pydantic AI ensures tool results conform to defined schemas, eliminating runtime type errors

Dependency injection system cleanly separates your Dagster connection logic from agent behavior for testable, maintainable code

Dagster + Pydantic AI Use Cases

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

Type-safe data pipelines: query Dagster with guaranteed response schemas, feeding validated data into downstream processing

API orchestration: chain multiple Dagster tool calls with Pydantic validation at each step to ensure data integrity end-to-end

Production monitoring: build validated alert agents that query Dagster and output structured, schema-compliant notifications

Testing and QA: use Pydantic AI's dependency injection to mock Dagster responses and write comprehensive agent tests

Dagster MCP Tools for Pydantic AI (6)

These 6 tools become available when you connect Dagster to Pydantic AI 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 Pydantic AI

Ready-to-use prompts you can give your Pydantic AI 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 Pydantic AI

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

MCPServerHTTP not found

Update: pip install --upgrade pydantic-ai

Dagster + Pydantic AI FAQ

Common questions about integrating Dagster MCP Server with Pydantic AI.

How does Pydantic AI discover MCP tools?

Create an MCPServerHTTP instance with the server URL. Pydantic AI connects, discovers all tools, and generates typed Python interfaces automatically.

Does Pydantic AI validate MCP tool responses?

Yes. When you define result types as Pydantic models, every tool response is validated against the schema. Invalid data raises a clear error instead of silently corrupting your pipeline.

Can I switch LLM providers without changing MCP code?

Absolutely. Pydantic AI abstracts the model layer — your Dagster MCP integration works identically with OpenAI, Anthropic, Google, or any supported provider.