How do I connect the Scaleway MCP Server to my AI client?

To connect Scaleway to your client (like Claude Desktop or Cursor), add the server command to your configuration file (e.g., 'claude_desktop_config.json'). You can run it locally using npx or deploy it to our secure managed cloud at MCPserver.in with one-click.

What security controls are available on the Scaleway integration?

The Scaleway MCP server keeps your API credentials secured in environment variables on your host or deployed cluster. It executes tools in a sandboxed runtime, requiring user confirmation for write, delete, or external publishing actions.

Does the Scaleway MCP Server support real-time data lookups?

Yes! By utilizing Model Context Protocol Resources, the Scaleway server queries active API endpoints on-demand, returning live context directly to the LLM context window without relying on outdated web caches.

What are the typical use cases for the Scaleway AI connector?

Common use cases include automating Scaleway data lookups, pulling active report profiles, updating statuses directly from chat inputs, and synchronizing project files with development environments.

What is the cost of running the Scaleway MCP integration?

Running the server locally on your machine is 100% free. If you deploy it to our fully-managed hosting in India for team access, it is covered under the Developer tier (₹999/mo) or our Pro plan with low-latency edge scaling.

Verified MCP Connector

Scaleway MCP Server

Expose Scaleway cloud elements, audit bare metal nodes, and manage S3-compatible objects.

Quick Answer / TL;DR

The Scaleway MCP server establishes a secure, local or remote JSON-RPC 2.0 communication tunnel, allowing AI models (like Claude or Cursor) to automatically discover and execute capabilities (tools, prompts, and resources) within the Scaleway ecosystem with extremely low latency.

Key Takeaways

Monitor bare metal capacities
Manage file storage parameters
Track network balance

Core Integration Concept

Connecting the model to Scaleway bypasses complex setup. The LLM can auto-discover what endpoints are active, what input variables are expected, and how answers will be delivered.

Verified Use Cases

Monitor bare metal capacities

Manage file storage parameters

Track network balance

Setup Overview

Connection Setup Checklist

Prepare Credentials: Obtain your Scaleway Token credentials directly from your Scaleway settings.
Update Config: Add the executable tool command structure directly to your Claude config file.
Restart & Confirm: Reload the desktop model client to complete the connection handshake sequence.

Sample Connection Schema

{
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "name": "execute_scaleway",
    "arguments": {
      "query": "status_check"
    }
  },
  "id": 1
}

Security Considerations

To guarantee perfect data isolation, safeguard the Scaleway Token credentials. Always run integrations in sandboxed contexts to block unsolicited access.

Best Practices

Configure exact resource boundaries for the Bare metal telemetry feature.
Configure exact resource boundaries for the Bucket actions feature.
Configure exact resource boundaries for the Load balancer metrics feature.
Configure exact resource boundaries for the Cost diagnostics feature.

Dr. Devashish Sen

Lead Systems & Protocol Architect, MCPserver India

Published: 2026-03-12

Updated: 2026-07-09

References & Technical Specifications

Required Auth Keys

Scaleway Token

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Related Connectors

→ AWS MCP Server → Google Cloud MCP Server → Azure MCP Server

Scaleway - FAQ

Contextual information and technical support details regarding Model Context Protocol integration