Dagster MCP Server for LlamaIndex 6 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Dagster as an MCP tool provider through Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.
ASK AI ABOUT THIS MCP SERVER
Vinkius supports streamable HTTP and SSE.
import asyncio
from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI
async def main():
# Your Vinkius token. get it at cloud.vinkius.com
mcp_client = BasicMCPClient("https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
mcp_tool_spec = McpToolSpec(client=mcp_client)
tools = await mcp_tool_spec.to_tool_list_async()
agent = FunctionAgent(
tools=tools,
llm=OpenAI(model="gpt-4o"),
system_prompt=(
"You are an assistant with access to Dagster. "
"You have 6 tools available."
),
)
response = await agent.run(
"What tools are available in Dagster?"
)
print(response)
asyncio.run(main())
* 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.
LlamaIndex agents combine Dagster tool responses with indexed documents for comprehensive, grounded answers. Connect 6 tools through Vinkius and query live data alongside vector stores and SQL databases in a single turn. ideal for hybrid search, data enrichment, and analytical workflows.
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 LlamaIndex 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 LlamaIndex via MCP
Follow these steps to integrate the Dagster MCP Server with LlamaIndex.
Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai
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
Why Use LlamaIndex with the Dagster MCP Server
LlamaIndex provides unique advantages when paired with Dagster through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Dagster tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Dagster tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Dagster, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Dagster tools were called, what data was returned, and how it influenced the final answer
Dagster + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Dagster MCP Server delivers measurable value.
Hybrid search: combine Dagster real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Dagster to augment indexed data with live information before generating user-facing responses
Knowledge base agents: build agents that maintain and update knowledge bases by periodically querying Dagster for fresh data
Analytical workflows: chain Dagster queries with LlamaIndex's data connectors to build multi-source analytical reports
Dagster MCP Tools for LlamaIndex (6)
These 6 tools become available when you connect Dagster to LlamaIndex 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 LlamaIndex
Ready-to-use prompts you can give your LlamaIndex 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 LlamaIndex
Common issues when connecting Dagster to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpDagster + LlamaIndex FAQ
Common questions about integrating Dagster MCP Server with LlamaIndex.
How does LlamaIndex connect to MCP servers?
Can I combine MCP tools with vector stores?
Does LlamaIndex support async MCP calls?
Connect Dagster with your favorite client
Step-by-step setup guides for every MCP-compatible client and framework:
Anthropic's native desktop app for Claude with built-in MCP support.
AI-first code editor with integrated LLM-powered coding assistance.
GitHub Copilot in VS Code with Agent mode and MCP support.
Purpose-built IDE for agentic AI coding workflows.
Autonomous AI coding agent that runs inside VS Code.
Anthropic's agentic CLI for terminal-first development.
Python SDK for building production-grade OpenAI agent workflows.
Google's framework for building production AI agents.
Type-safe agent development for Python with first-class MCP support.
TypeScript toolkit for building AI-powered web applications.
TypeScript-native agent framework for modern web stacks.
Python framework for orchestrating collaborative AI agent crews.
Leading Python framework for composable LLM applications.
Data-aware AI agent framework for structured and unstructured sources.
Microsoft's framework for multi-agent collaborative conversations.
Connect Dagster to LlamaIndex
Get your token, paste the configuration, and start using 6 tools in under 2 minutes. No API key management needed.