How do I find my JD Cloud Access Key and Secret Key?

Log in to the [JD Cloud Console](https://console.jdcloud.com/), navigate to [Account Management] -> [Access Key Management] to find or create your unique credentials.

What regions are supported?

JD Cloud supports multiple regions including `cn-north-1` (Beijing), `cn-east-1` (Suzhou), `cn-east-2` (Shanghai), and `cn-south-1` (Guangzhou). Ensure you specify the correct region where your resources are located.

Does this server handle JDCLOUD2-HMAC-SHA256 signatures?

Yes! The server automatically calculates the required Signature v2 (JDCLOUD2-HMAC-SHA256) for every request using your provided Secret Key, ensuring high security for your cloud orchestration.

JD Cloud / 京东云 MCP Server for Pydantic AI 8 tools — connect in under 2 minutes

Name: JD Cloud / 京东云
Availability: InStock
Author: Vinkius

ai.pydantic.dev/mcp

Built by Vinkius GDPR 8 Tools SDK

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

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

Open in ChatGPT Open in Claude Open in Perplexity

Vinkius supports streamable HTTP and SSE.

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 JD Cloud / 京东云 "
            "(8 tools)."
        ),
    )

    result = await agent.run(
        "What tools are available in JD Cloud / 京东云?"
    )
    print(result.data)

asyncio.run(main())

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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 JD Cloud / 京东云 MCP Server

Empower your AI agent to orchestrate your cloud infrastructure and supply chain assets with JD Cloud (京东云), the premier cloud service provider by JD.com. By connecting JD Cloud to your agent, you transform complex virtual machine management, storage bucket auditing, and billing analysis into a natural conversation. Your agent can instantly retrieve VM instance details, list Object Storage Service (OSS) buckets, audit VPC networks, and retrieve comprehensive billing summaries without you ever needing to navigate the comprehensive JD Cloud Console. Whether you are managing e-commerce backend resources or coordinating high-volume digital distribution, your agent acts as a real-time cloud operations assistant, providing accurate results from a single, authorized source.

Pydantic AI validates every JD Cloud / 京东云 tool response against typed schemas, catching data inconsistencies at build time. Connect 8 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

Compute Orchestration — List virtual machines, retrieve detailed instance metadata, and monitor operational status.
Storage Auditing — List Object Storage Service (OSS) buckets and manage cloud disk resources across regions.
Network Management — Retrieve details for Virtual Private Clouds (VPC) and coordinate network topology audits.
Financial Auditing — Retrieve comprehensive billing summaries for specific time ranges to maintain cost control.
Operational Monitoring — Verify project connectivity, active regions, and retrieve IAM account profile details.

The JD Cloud / 京东云 MCP Server exposes 8 tools through the Vinkius. Connect it to Pydantic AI 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 JD Cloud / 京东云 to Pydantic AI via MCP

Follow these steps to integrate the JD Cloud / 京东云 MCP Server with Pydantic AI.

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 8 tools from JD Cloud / 京东云 with type-safe schemas

Why Use Pydantic AI with the JD Cloud / 京东云 MCP Server

Pydantic AI provides unique advantages when paired with JD Cloud / 京东云 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 JD Cloud / 京东云 integration code

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

Dependency injection system cleanly separates your JD Cloud / 京东云 connection logic from agent behavior for testable, maintainable code

JD Cloud / 京东云 + Pydantic AI Use Cases

Practical scenarios where Pydantic AI combined with the JD Cloud / 京东云 MCP Server delivers measurable value.

Type-safe data pipelines: query JD Cloud / 京东云 with guaranteed response schemas, feeding validated data into downstream processing

API orchestration: chain multiple JD Cloud / 京东云 tool calls with Pydantic validation at each step to ensure data integrity end-to-end

Production monitoring: build validated alert agents that query JD Cloud / 京东云 and output structured, schema-compliant notifications

Testing and QA: use Pydantic AI's dependency injection to mock JD Cloud / 京东云 responses and write comprehensive agent tests

JD Cloud / 京东云 MCP Tools for Pydantic AI (8)

These 8 tools become available when you connect JD Cloud / 京东云 to Pydantic AI via MCP:

get_account_profile

Get IAM user info

get_billing_summary

Get billing overview

get_vm_detail

Get VM metadata

list_cicd_pipelines

List DevOps pipelines

list_cloud_disks

List block storage disks

list_oss_buckets

List storage buckets

list_vm_instances

List virtual machines

list_vpc_networks

List VPC networks

Example Prompts for JD Cloud / 京东云 in Pydantic AI

Ready-to-use prompts you can give your Pydantic AI agent to start working with JD Cloud / 京东云 immediately.

"List all my running virtual machines in region 'cn-north-1'."

"Check our JD Cloud billing summary from October 1st to October 15th."

"Show me the list of Object Storage (OSS) buckets in my account."

Troubleshooting JD Cloud / 京东云 MCP Server with Pydantic AI

Common issues when connecting JD Cloud / 京东云 to Pydantic AI through the Vinkius, and how to resolve them.

MCPServerHTTP not found

Update: pip install --upgrade pydantic-ai

JD Cloud / 京东云 + Pydantic AI FAQ

Common questions about integrating JD Cloud / 京东云 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 JD Cloud / 京东云 MCP integration works identically with OpenAI, Anthropic, Google, or any supported provider.