Conduit MCP Server for LlamaIndex 8 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Conduit 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 Conduit. "
"You have 8 tools available."
),
)
response = await agent.run(
"What tools are available in Conduit?"
)
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 Conduit MCP Server
Connect your AI agent seamlessly with Conduit, the modern data integration and synchronization platform. Utilizing natural language interactions, users can instruct the AI to oversee active streaming health, check connectors, and extract pipeline logs without accessing the conventional web dashboard interfaces.
LlamaIndex agents combine Conduit tool responses with indexed documents for comprehensive, grounded answers. Connect 8 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
- Pipeline Management — Request status overviews of active, paused, or degraded data integration pipelines efficiently.
- Connector Auditing — Ask the agent to locate specific connectors (source or destination) mapped to your critical infrastructure.
- Log Evaluation — Fetch recent application logs or streaming output reports via conversation to debug integration errors on the fly.
The Conduit MCP Server exposes 8 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 Conduit to LlamaIndex via MCP
Follow these steps to integrate the Conduit 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 8 tools from Conduit
Why Use LlamaIndex with the Conduit MCP Server
LlamaIndex provides unique advantages when paired with Conduit through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Conduit tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Conduit tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Conduit, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Conduit tools were called, what data was returned, and how it influenced the final answer
Conduit + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Conduit MCP Server delivers measurable value.
Hybrid search: combine Conduit real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Conduit 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 Conduit for fresh data
Analytical workflows: chain Conduit queries with LlamaIndex's data connectors to build multi-source analytical reports
Conduit MCP Tools for LlamaIndex (8)
These 8 tools become available when you connect Conduit to LlamaIndex via MCP:
get_run_status
Returns detailed status, timing, and error information. Retrieve the current status of a specific workflow run
get_workflow
Returns source, destination, and current status. Retrieve detailed information about a specific workflow
list_available_destinations
Retrieve available data destination connector types supported by Conduit
list_available_sources
Retrieve available data source connector types supported by Conduit
list_connections
Retrieve a list of all active source and destination connections
list_workflow_runs
Returns the execution history with status and timestamps for each run. Retrieve the history of runs for a specific workflow
list_workflows
Use this as a starting point to discover workflow IDs for subsequent operations. Retrieve a list of all data integration workflows in Conduit
trigger_workflow
Use list_workflows first to find the workflow ID. Manually trigger a run for a specific workflow
Example Prompts for Conduit in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with Conduit immediately.
"Retrieve the current status of all major pipelines running in the production Conduit instance."
"Check if there's a configured destination connector named 's3-analytics-bucket' and briefly describe its configuration parameters."
"Pause the pipeline 'MySQL-to-Kafka' immediately."
Troubleshooting Conduit MCP Server with LlamaIndex
Common issues when connecting Conduit to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpConduit + LlamaIndex FAQ
Common questions about integrating Conduit 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 Conduit 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 Conduit to LlamaIndex
Get your token, paste the configuration, and start using 8 tools in under 2 minutes. No API key management needed.