Orkes Conductor 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 Orkes Conductor as an MCP tool provider through Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.
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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 Orkes Conductor. "
"You have 6 tools available."
),
)
response = await agent.run(
"What tools are available in Orkes Conductor?"
)
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 Orkes Conductor MCP Server
Connect your Orkes Conductor cluster to any AI agent and get full visibility into your workflow orchestration layer — definitions, running instances, task states, and execution history.
LlamaIndex agents combine Orkes Conductor 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
- Workflow Definitions — List all registered workflow definitions with versions and descriptions, or inspect a specific workflow's graph schema with tasks, operators, and branching logic
- Task Definitions — List all registered task definitions available for orchestration within your workflows
- Running Instances — List actively running workflow instances filtered by workflow name to monitor what's currently executing
- Execution Details — Get deep state details for any workflow execution including input/output mappings, task-by-task trace histories, and exceptions
- Workflow Search — Search across all workflow executions using Elasticsearch queries, filtering by status, correlation ID, or workflow type
The Orkes Conductor 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 Orkes Conductor to LlamaIndex via MCP
Follow these steps to integrate the Orkes Conductor 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 Orkes Conductor
Why Use LlamaIndex with the Orkes Conductor MCP Server
LlamaIndex provides unique advantages when paired with Orkes Conductor through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Orkes Conductor tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Orkes Conductor tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Orkes Conductor, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Orkes Conductor tools were called, what data was returned, and how it influenced the final answer
Orkes Conductor + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Orkes Conductor MCP Server delivers measurable value.
Hybrid search: combine Orkes Conductor real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Orkes Conductor 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 Orkes Conductor for fresh data
Analytical workflows: chain Orkes Conductor queries with LlamaIndex's data connectors to build multi-source analytical reports
Orkes Conductor MCP Tools for LlamaIndex (6)
These 6 tools become available when you connect Orkes Conductor to LlamaIndex via MCP:
get_execution
Get deep state details of a specific Workflow Execution
get_workflow_def
Get a specific Workflow Definition explicitly by name
list_running
List active, running workflow instances by explicit workflow name
list_task_defs
List all explicitly registered Task Definitions via Conductor API
list_workflow_defs
List all registered overarching Workflow Definitions via Orkes API
search_workflows
Perform an elastic Search across all Workflow executions
Example Prompts for Orkes Conductor in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with Orkes Conductor immediately.
"Show me all registered workflow definitions."
"Are there any failed workflows in the last 24 hours?"
"How many instances of the order-processing workflow are currently running?"
Troubleshooting Orkes Conductor MCP Server with LlamaIndex
Common issues when connecting Orkes Conductor to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpOrkes Conductor + LlamaIndex FAQ
Common questions about integrating Orkes Conductor 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 Orkes Conductor 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 Orkes Conductor to LlamaIndex
Get your token, paste the configuration, and start using 6 tools in under 2 minutes. No API key management needed.