Scaleway MCP Server for Pydantic AIGive Pydantic AI instant access to 3 tools to Create Instance, List Instances, Perform Instance Action

Name: Scaleway
Price: 29 USD
Availability: InStock
Rating: 5.0 (1 reviews)
Author: Vinkius

GDPR Free for Subscribers

Pydantic AI brings type-safe agent development to Python with first-class MCP support. Connect Scaleway through Vinkius and every tool is automatically validated against Pydantic schemas. catch errors at build time, not in production.

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The Scaleway MCP Server for Pydantic AI is a standout in the Developer Tools category — giving your AI agent 3 tools to work with, ready to go from day one.

Built for AI Agents by Vinkius

Vinkius delivers Streamable HTTP and SSE to any MCP client

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python

import asyncio
from pydantic_ai import Agent
from pydantic_ai.mcp import MCPServerHTTP

async def main():
    # Your Vinkius token. get it at cloud.vinkius.com
    server = MCPServerHTTP(url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")

    agent = Agent(
        model="openai:gpt-4o",
        mcp_servers=[server],
        system_prompt=(
            "You are an assistant with access to Scaleway "
            "(3 tools)."
        ),
    )

    result = await agent.run(
        "What tools are available in Scaleway?"
    )
    print(result.data)

asyncio.run(main())

Fully ManagedVinkius Servers

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Stream every event to Splunk, Datadog, or your own webhook in real-time

* 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.

Pydantic AI validates every Scaleway tool response against typed schemas, catching data inconsistencies at build time. Connect 3 tools through Vinkius and switch between OpenAI, Anthropic, or Gemini without changing your integration code. full type safety, structured output guarantees, and dependency injection for testable agents.

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 Pydantic AI 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 Pydantic AI

When Pydantic AI 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

Create instance on Scaleway

Create a new Scaleway instance (server)

list

List instances on Scaleway

List Scaleway instances (servers) in a specific zone

perform

Perform instance action on Scaleway

Perform an action on a Scaleway instance (e.g., poweron, poweroff)

Connect Scaleway to Pydantic AI via MCP

Follow these steps to wire Scaleway into Pydantic AI. The entire setup takes under two minutes — your credentials stay safe behind Vinkius.

Install Pydantic AI

Run pip install pydantic-ai

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 3 tools from Scaleway with type-safe schemas

Why Use Pydantic AI with the Scaleway MCP Server

Pydantic AI provides unique advantages when paired with Scaleway through the Model Context Protocol.

Full type safety: every MCP tool response is validated against Pydantic models, catching data inconsistencies before they reach your application

Model-agnostic architecture. switch between OpenAI, Anthropic, or Gemini without changing your Scaleway integration code

Structured output guarantee: Pydantic AI ensures tool results conform to defined schemas, eliminating runtime type errors

Dependency injection system cleanly separates your Scaleway connection logic from agent behavior for testable, maintainable code

Scaleway + Pydantic AI Use Cases

Practical scenarios where Pydantic AI combined with the Scaleway MCP Server delivers measurable value.

Type-safe data pipelines: query Scaleway with guaranteed response schemas, feeding validated data into downstream processing

API orchestration: chain multiple Scaleway tool calls with Pydantic validation at each step to ensure data integrity end-to-end

Production monitoring: build validated alert agents that query Scaleway and output structured, schema-compliant notifications

Testing and QA: use Pydantic AI's dependency injection to mock Scaleway responses and write comprehensive agent tests

Example Prompts for Scaleway in Pydantic AI

Ready-to-use prompts you can give your Pydantic AI 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 Pydantic AI

Common issues when connecting Scaleway to Pydantic AI through Vinkius, and how to resolve them.

MCPServerHTTP not found

Update: pip install --upgrade pydantic-ai

Scaleway + Pydantic AI FAQ

Common questions about integrating Scaleway MCP Server with Pydantic AI.

How does Pydantic AI discover MCP tools?

Create an MCPServerHTTP instance with the server URL. Pydantic AI connects, discovers all tools, and generates typed Python interfaces automatically.

Does Pydantic AI validate MCP tool responses?

Yes. When you define result types as Pydantic models, every tool response is validated against the schema. Invalid data raises a clear error instead of silently corrupting your pipeline.

Can I switch LLM providers without changing MCP code?

Absolutely. Pydantic AI abstracts the model layer. your Scaleway MCP integration works identically with OpenAI, Anthropic, Google, or any supported provider.