How to Use the Conductor (Netflix OSS) MCP in LlamaIndex

Q: Can LlamaIndex query live Conductor (Netflix OSS) execution logs in real-time?

Yes, the agent calls gettasklogs dynamically during query execution. It passes this live text to the LLM to ground its answers in your current system state. You don't have to manually inspect the console.

Index your Netflix Conductor workflow definitions and live execution logs into LlamaIndex vector stores.

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Connect Conductor (Netflix OSS) MCP to LlamaIndex

Create your Vinkius account to connect Conductor (Netflix OSS) to LlamaIndex and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

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Setup Conductor (Netflix OSS) with LlamaIndex

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Index Conductor definitions into LlamaIndex vector stores

Feeding your LlamaIndex agent configuration schemas is easy with this server's `get_workflow_definitions` and `get_task_definitions` tools. The agent converts these schemas into searchable vector embeddings for your RAG pipeline. This setup lets you query your Conductor configuration using LlamaIndex natural language queries. Your agent answers questions about how tasks run based on actual code definitions, not old documentation.

Search live Conductor logs using LlamaIndex queries

By providing `get_task_logs` and `search_workflows_v2`, this server feeds live execution data directly into your LlamaIndex query engine. When a workflow fails, the agent pulls the exact log output and matches it against historic failure patterns in your index. You get instant troubleshooting suggestions grounded in your LlamaIndex knowledge base. The agent reads the exact execution path to find where the task stalled without manual log diving.

Analyze queue depths using LlamaIndex agents

Your LlamaIndex agent can monitor system health using the `get_queue_size` and `get_all_queues` tools. The agent indexes these metrics to detect bottlenecks and suggest configuration updates. By comparing current queue size with historical data in your vector store, the agent spots anomalies early. It gives your team clear recommendations to rebalance Conductor tasks using indexed historical trends.

Setup guide

Set up Conductor (Netflix OSS) MCP in LlamaIndex

Prerequisites

Python 3.10+ installed
llama-index-tools-mcp package
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai. The MCP tools package provides BasicMCPClient and McpToolSpec.
2

Connect with BasicMCPClient
Point BasicMCPClient to your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports SSE and Streamable HTTP transports.
3

Convert to LlamaIndex tools
Call mcp_tool_spec.to_tool_list_async() to convert all Conductor (Netflix OSS) MCP tools into native FunctionTool objects that any LlamaIndex agent can use.
4

Run with any LLM
Create a FunctionAgent with the tools and your preferred LLM. Swap OpenAI for Anthropic, Gemini, or any LlamaIndex-supported provider.

agent.py

from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI

# Connect to the MCP
mcp_client = BasicMCPClient(
    "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)
mcp_tool_spec = McpToolSpec(client=mcp_client)

# Convert MCP tools to LlamaIndex tools
tools = await mcp_tool_spec.to_tool_list_async()

# Create and run the agent
agent = FunctionAgent(
    tools=tools,
    llm=OpenAI(model="gpt-4o"),
    system_prompt="You have access to Conductor (Netflix OSS) tools.",
)
response = await agent.run("List recent Conductor (Netflix OSS) data")

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Conductor (Netflix OSS). All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

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Common questions about Conductor (Netflix OSS) MCP in LlamaIndex

Use BasicMCPClient to connect to the server, then fetch definitions using get_workflow_definitions. Convert these outputs into document nodes and index them directly into your vector store.

Yes, the agent calls get_task_logs dynamically during query execution. It passes this live text to the LLM to ground its answers in your current system state. You don't have to manually inspect the console.

The Vinkius platform handles the underlying connection pooling. Your LlamaIndex agent just calls tools like search_workflows without worrying about connection drops or socket timeouts.

Install the package using pip install llama-index-tools-mcp. Then initialize BasicMCPClient and pass the tools to your FunctionAgent using McpToolSpec.

Yes, all execution logs and task payloads are processed in an ephemeral V8 sandbox. Your task definitions and execution logs are isolated in a secure sandbox that is destroyed immediately after your query finishes.

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