Scaleway MCP Server for LangChainGive LangChain instant access to 3 tools to Create Instance, List Instances, Perform Instance Action
LangChain is the leading Python framework for composable LLM applications. Connect Scaleway 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 for LangChain
The Scaleway MCP Server for LangChain is a standout in the Developer Tools category — giving your AI agent 3 tools to work with, ready to go from day one.
Vinkius delivers Streamable HTTP and SSE to any MCP client
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({
"scaleway": {
"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 Scaleway, 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 Scaleway MCP Server
Connect your Scaleway account to any AI agent to manage your cloud infrastructure through natural language. This server provides direct access to the Scaleway Instances API.
LangChain's ecosystem of 500+ components combines seamlessly with Scaleway through native MCP adapters. Connect 3 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
- Instance Discovery — List all virtual machines across different availability zones (e.g., fr-par-1, nl-ams-1)
- Provisioning — Create new instances by specifying names, commercial types (like DEV1-S), and image IDs
- Power Management — Remotely power on, power off, or reboot your servers
- Lifecycle Control — Terminate instances that are no longer needed directly from the chat
The Scaleway MCP Server exposes 3 tools through the Vinkius. Connect it to LangChain in under two minutes — credentials fully managed, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.
All 3 Scaleway tools available for LangChain
When LangChain connects to Scaleway through Vinkius, your AI agent gets direct access to every tool listed below — spanning cloud-computing, virtual-machines, bare-metal, and more. Every call runs in a secure, isolated environment with full audit visibility. Beyond a simple connection, you get real-time monitoring of agent activity, enterprise governance, and optimized token usage.
Create instance on Scaleway
Create a new Scaleway instance (server)
List instances on Scaleway
List Scaleway instances (servers) in a specific zone
Perform instance action on Scaleway
Perform an action on a Scaleway instance (e.g., poweron, poweroff)
Connect Scaleway to LangChain via MCP
Follow these steps to wire Scaleway into LangChain. The entire setup takes under two minutes — your credentials stay safe behind Vinkius.
Install dependencies
pip install langchain langchain-mcp-adapters langgraph langchain-openaiReplace the token
[YOUR_TOKEN_HERE] with your Vinkius tokenRun the agent
python agent.pyExplore tools
Why Use LangChain with the Scaleway MCP Server
LangChain provides unique advantages when paired with Scaleway through the Model Context Protocol.
The largest ecosystem of integrations, chains, and agents. combine Scaleway 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 Scaleway queries for multi-turn workflows
Scaleway + LangChain Use Cases
Practical scenarios where LangChain combined with the Scaleway MCP Server delivers measurable value.
RAG with live data: combine Scaleway tool results with vector store retrievals for answers grounded in both real-time and historical data
Autonomous research agents: LangChain agents query Scaleway, synthesize findings, and generate comprehensive research reports
Multi-tool orchestration: chain Scaleway tools with web scrapers, databases, and calculators in a single agent run
Production monitoring: use LangSmith to trace every Scaleway tool call, measure latency, and optimize your agent's performance
Example Prompts for Scaleway in LangChain
Ready-to-use prompts you can give your LangChain agent to start working with Scaleway immediately.
"List all my instances in the Paris zone (fr-par-1)."
"Create a new DEV1-S instance named 'staging-app' in fr-par-1 using the Ubuntu image."
"Reboot the server with ID 550e8400-e29b-41d4-a716-446655440000 in nl-ams-1."
Troubleshooting Scaleway MCP Server with LangChain
Common issues when connecting Scaleway to LangChain through Vinkius, and how to resolve them.
MultiServerMCPClient not found
pip install langchain-mcp-adaptersScaleway + LangChain FAQ
Common questions about integrating Scaleway 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?
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