Conduit MCP Server for LangChain 8 tools — connect in under 2 minutes
LangChain is the leading Python framework for composable LLM applications. Connect Conduit through Vinkius and LangChain agents can call every tool natively. combine them with retrievers, memory, and output parsers for sophisticated AI pipelines.
ASK AI ABOUT THIS MCP SERVER
Vinkius supports streamable HTTP and SSE.
import asyncio
from langchain_mcp_adapters.client import MultiServerMCPClient
from langchain_openai import ChatOpenAI
from langgraph.prebuilt import create_react_agent
async def main():
# Your Vinkius token. get it at cloud.vinkius.com
async with MultiServerMCPClient({
"conduit": {
"transport": "streamable_http",
"url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp",
}
}) as client:
tools = client.get_tools()
agent = create_react_agent(
ChatOpenAI(model="gpt-4o"),
tools,
)
response = await agent.ainvoke({
"messages": [{
"role": "user",
"content": "Using Conduit, show me what tools are available.",
}]
})
print(response["messages"][-1].content)
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.
LangChain's ecosystem of 500+ components combines seamlessly with Conduit through native MCP adapters. Connect 8 tools via Vinkius and use ReAct agents, Plan-and-Execute strategies, or custom agent architectures. with LangSmith tracing giving full visibility into every tool call, latency, and token cost.
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 LangChain 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 LangChain via MCP
Follow these steps to integrate the Conduit MCP Server with LangChain.
Install dependencies
Run pip install langchain langchain-mcp-adapters langgraph langchain-openai
Replace the token
Replace [YOUR_TOKEN_HERE] with your Vinkius token
Run the agent
Save the code and run python agent.py
Explore tools
The agent discovers 8 tools from Conduit via MCP
Why Use LangChain with the Conduit MCP Server
LangChain provides unique advantages when paired with Conduit through the Model Context Protocol.
The largest ecosystem of integrations, chains, and agents. combine Conduit MCP tools with 500+ LangChain components
Agent architecture supports ReAct, Plan-and-Execute, and custom strategies with full MCP tool access at every step
LangSmith tracing gives you complete visibility into tool calls, latencies, and token usage for production debugging
Memory and conversation persistence let agents maintain context across Conduit queries for multi-turn workflows
Conduit + LangChain Use Cases
Practical scenarios where LangChain combined with the Conduit MCP Server delivers measurable value.
RAG with live data: combine Conduit tool results with vector store retrievals for answers grounded in both real-time and historical data
Autonomous research agents: LangChain agents query Conduit, synthesize findings, and generate comprehensive research reports
Multi-tool orchestration: chain Conduit tools with web scrapers, databases, and calculators in a single agent run
Production monitoring: use LangSmith to trace every Conduit tool call, measure latency, and optimize your agent's performance
Conduit MCP Tools for LangChain (8)
These 8 tools become available when you connect Conduit to LangChain 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 LangChain
Ready-to-use prompts you can give your LangChain 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 LangChain
Common issues when connecting Conduit to LangChain through the Vinkius, and how to resolve them.
MultiServerMCPClient not found
pip install langchain-mcp-adaptersConduit + LangChain FAQ
Common questions about integrating Conduit MCP Server with LangChain.
How does LangChain connect to MCP servers?
langchain-mcp-adapters to create an MCP client. LangChain discovers all tools and wraps them as native LangChain tools compatible with any agent type.Which LangChain agent types work with MCP?
Can I trace MCP tool calls in LangSmith?
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 LangChain
Get your token, paste the configuration, and start using 8 tools in under 2 minutes. No API key management needed.