JD Cloud Infrastructure MCP for AI. Manage VMs, disks, and databases in conversation.
Works with every AI agent you already use
…and any MCP-compatible client








Connect to your AI in seconds.
JD Cloud Infrastructure MCP connects your AI agent directly to JD Cloud's full suite of operations tools. You can manage VMs, disks, and databases from a single chat interface.
List instances, start/stop machines, monitor CPU usage, and check Object Storage buckets without leaving your development environment.
What your AI can do
Describe cloud disk
Gets detailed info on a specific cloud disk volume.
Describe vm instance
Retrieves detailed information about a specific virtual machine instance.
Describe metric data
Pulls monitoring metric data for any given cloud resource.
You can list all virtual machines and send commands to start, stop, or reboot individual instances.
Query time-series data for CPU usage, network traffic, and disk performance on any resource.
Get full listings of cloud disks and Object Storage Service buckets in your region.
List all Elastic IP allocations and confirm their current association status.
Ask an AI about this
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JD Cloud Infrastructure: 11 Tools
Use these tools to query resource metrics, list all cloud components, and execute state changes across JD Cloud resources.
Make your AI actually useful.
Add this MCP to Claude, Cursor, or Windsurf and your AI stops guessing. It gets real tools to look things up, take action, and handle the stuff you keep doing by hand.
Start using JD Cloud Infrastructure on VinkiusDescribe Cloud Disk
Gets detailed info on a specific cloud disk volume.
Describe Vm Instance
Retrieves detailed information about a specific virtual machine instance.
Describe Metric Data
Pulls monitoring metric data for any given cloud resource.
List Oss Buckets
Gives a list of all Object Storage Service buckets.
List Cloud Disks
Provides an inventory of all cloud disk volumes in your region.
List Elastic Ips
Lists every Elastic IP address allocated to your account.
List Vm Instances
Lists all virtual machine instances currently running or stopped in the JD Cloud area.
List Rds Instances
Shows an inventory of all managed database (RDS) instances in your region.
Reboot Vm Instance
Forces a reboot cycle on an existing VM instance.
Start Vm Instance
Starts up a virtual machine that is currently stopped.
Stop Vm Instance
Shuts down a running VM instance to save costs or for maintenance.
Security and governance baked right in.
Pick your AI client below to get set up. Just create a Vinkius account, subscribe, and you're instantly up and running. We handle the entire backend infrastructure, delivering out-of-the-box support for HTTPS Streamable, SSE, and OAuth2—zero messy routing required.
Choose How to Get Started
Build a custom MCP for your own tools, or connect a ready-made integration from our catalog.
Build Your Own
Turn any API into an MCP. Import a spec, define Agent Skills, or deploy with MCPFusion.
- Import from OpenAPI, Swagger, or YAML specs
- Create Agent Skills with progressive disclosure
- Deploy to edge with MCPFusion framework
- Built in DLP, auth, and compliance on every call
- Real time usage dashboard and cost metering
- Publish to catalog or keep private
Make Your AI Do More
Start with JD Cloud Infrastructure, then connect any of our 5,100+ other servers whenever your AI needs more. One click, no limits.
- Use this MCP plus 5,100+ others, all in one place
- Add new capabilities to your AI anytime you want
- Every connection is secured and compliant automatically
- Track usage and costs across all your servers
- Works with Claude, ChatGPT, Cursor, and more
- New servers added to the catalog every week
Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by JD Cloud. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.
VINKIUS INFRASTRUCTURE
Cloud Hosted
Managed infra
V8 Isolated
Sandboxed per request
Zero-Trust Proxy
No stored credentials
DLP Enforced
Policy on every call
GDPR Compliant
EU data residency
Token Compression
~60% cost reduction
Works with Claude, ChatGPT, Cursor, and more
The Model Context Protocol standardizes how applications expose capabilities to LLMs. Instead of operating in isolation, your AI gains direct access to external platforms, live data, and real-world actions through secure, standardized connections.
This connection provides 11 powerful capabilities that interface natively with Claude, ChatGPT, Cursor, and other compatible AI platforms. No middleware. No custom integration required.
The Pain Point: Jumping Between Dashboards
Right now, diagnosing a cloud issue is a chore. You start on the main dashboard, find an alert about high CPU usage on VM-XYZ. Then you copy that ID and paste it into the Monitoring console to check metrics. If the disk is suspect, you have to open another tab just for disks, get the volume ID, and then cross-reference everything manually.
With this MCP, your agent does all of that work in one go. You ask: 'Why is VM-XYZ slow?' It automatically checks the CPU metrics using `describe_metric_data`, verifies the disk health with `describe_cloud_disk` if needed, and even tells you which resources are connected. The entire process happens without you leaving your chat window.
JD Cloud Infrastructure MCP: Full Control Over Resources
You no longer have to manually run a list command, grab an ID, and then feed it into another service. Instead, you simply ask the agent to 'List all active VMs and their associated disks.' The response is clean, comprehensive, and ready for action.
It’s about control—control over every state change, from stopping a machine with `stop_vm_instance` to getting full inventory via `list_rds_instances`. You run the operation, you get the status. Period.
What your AI can actually do with this
Managing cloud infrastructure usually means clicking through half a dozen different dashboards. You’re looking at VM status in one tab, disk metrics in another, and networking logs somewhere else. This MCP changes that. It gives your AI agent direct access to the core control plane for JD Cloud—the enterprise backbone running massive e-commerce operations.
Through this connection, you can query resource states, check performance graphs, and perform lifecycle actions using natural conversation. Whether it’s checking if a virtual machine needs a reboot or listing all available Object Storage buckets, your agent handles the API calls instantly. You get to skip the console hopping entirely.
Since Vinkius hosts this MCP, your agent connects once and gains access to JD Cloud's full operational toolkit, right alongside thousands of other services. It’s about getting immediate answers and executing changes without ever touching a cloud provider dashboard.
019d844a-a417-714f-b904-e9bea6a4b9e1 Here's how it actually works
The bottom line is: Your AI client handles all the complex authentication and API calls behind the scenes; you just talk to it.
Navigate to the JD Cloud Console and generate your IAM Access Keys.
Plug your Access Key, Secret Key, and Region ID into Vurb.
The MCP engine builds the required JDCLOUD2-HMAC-SHA256 signature locally for every single request.
Who is this actually for?
This MCP is built for people who spend their time staring at dashboards. It's for the DevOps engineer stuck in a multi-hour deployment cycle, or the SRE team member needing real-time metrics on an outage. If you’re tired of copy-pasting IDs between consoles, this is for you.
Runs diagnostics when things break. Needs to quickly run describe_metric_data on a failing service and then use reboot_vm_instance if necessary.
Manages the deployment pipeline, needing to check resource availability by using list_rds_instances before provisioning new services.
Audits existing infrastructure. Needs to list all components like disks (list_cloud_disks) and verify network reachability via list_elastic_ips.
What Changes When You Connect
You manage the full VM lifecycle—start, stop, rebooting instances—all through chat commands. No need to jump between the Instance Dashboard and the Compute API.
Get immediate health data by querying metrics using describe_metric_data for any resource. This is faster than pulling graphs from a separate monitoring tool.
Inventory management gets simpler. Use list_cloud_disks or list_oss_buckets to quickly get counts and IDs without running complex search queries in the console.
Handle network issues fast. You can use list_elastic_ips to verify IP allocations, then cross-reference that data when checking a VM's status via describe_vm_instance.
The agent handles credentials and signatures automatically. You just tell it what you need; it figures out the complex API calls using your Access Key and Secret Key.
See it in action
Need to find out why a service is running slow.
The agent gets pinged: 'Check CPU usage for my primary web server.' It immediately uses describe_metric_data on the target VM, finds high load spikes, and reports back that the machine needs scaling. No manual metric checks needed.
Deploying a new microservice requires a clean slate.
An engineer asks to provision a test environment: 'List all running VMs, then stop any non-critical ones.' The agent uses list_vm_instances and executes multiple state changes with the necessary tools.
Investigating data loss after an unexpected shutdown.
The team leader asks to review disks: 'Show me all attached cloud disks for this project.' The agent runs list_cloud_disks, giving a complete list of potential storage sources.
Preparing for peak traffic season maintenance window.
A team member needs to verify network readiness. They run 'List all IPs and check which are associated.' The agent uses list_elastic_ips for a complete audit before the deployment starts.
The honest tradeoffs
Trying to list everything in one go.
A user tries to prompt, 'Show me all data about my infrastructure.' The agent fails because it can't determine if you mean VMs, disks, or buckets, leading to an ambiguous response.
Be specific. Ask for discrete lists: 'First, run list_vm_instances. Second, list the disks using list_cloud_disks.' Breaking down complex requests makes it reliable.
Mixing up VM states.
A user runs a vague command like 'Turn off that machine.' The agent might guess incorrectly, stopping a critical component instead of the intended test server.
Always use explicit state commands. Tell it to stop_vm_instance or start_vm_instance. Use describe_vm_instance first so you know exactly which ID you're talking about.
Ignoring the storage layer.
A user only checks VM status but forgets that data lives in Object Storage. They assume the machine failing means all data is lost, leading to panic and unnecessary manual audits.
Always check the storage side of things. Run list_oss_buckets alongside checking the VMs so you know where your raw data actually resides.
When It Fits, When It Doesn't
Use this MCP if your job involves constant, repetitive interaction with multiple cloud resource types—VMs, disks, databases, and network IPs. If you frequently have to switch between a monitoring console, an instance manager, and a storage bucket list just to diagnose a single outage, this is what you need. Don't use it if you only need to manage one specific service (like just RDS). For that, a dedicated database tool might be cleaner. However, if your pain point is the cross-referencing of resources—e.g., 'Does this disk belong to this VM, and what was its CPU load last week?'—then this MCP handles all those dependencies in one workflow.
Questions you might have
How do I check CPU usage using describe_metric_data? +
You pass the resource ID and specify the metric type (e.g., cpu.util). The agent returns time-series data, showing averages and peaks over the requested period.
What is the difference between stop_vm_instance and list_vm_instances? +
list_vm_instances just shows you what's there. stop_vm_instance actively changes the state, shutting down a running machine.
Can I use describe_cloud_disk to find out which VM it belongs to? +
The function gives detailed information about the disk itself (size, type). You must cross-reference that output with describe_vm_instance to confirm attachment.
Do I need to manually manage my access keys for list_elastic_ips? +
No. The MCP handles the complex authentication and signature generation using your provided Access Key and Secret Key automatically, so you just talk to it.
When I use `start_vm_instance` or `stop_vm_instance`, how do I handle common failure codes? +
The MCP returns the specific cloud error code and message. If a state change fails, check the detailed logs using describe_vm_instance; this pinpoints whether it's an invalid resource ID or a permission issue.
Do I hit rate limits when repeatedly calling `list_oss_buckets`? +
Yes, API rate limiting applies. While the MCP manages some retries automatically, making too many calls in quick succession can fail. For high-volume checks, consider batching your requests.
If my agent runs `describe_rds_instances` and doesn't see a database, what permissions am I missing? +
You need read access on the specific RDS resource group. The visibility of any resource is strictly tied to the IAM keys you provide; the AI client can only report what your credentials allow it to see.
Can I filter results when calling `list_vm_instances` by tags or criteria? +
Yes, you pass filters as arguments. To narrow down the scope of virtual machines, include specific tag keys and values in your request to get a highly targeted list.
Is the JDCLOUD2-HMAC-SHA256 signing handled automatically? +
Yes. The MCP engine locally derives signing keys through HMAC chains (date → region → service → jdcloud2_request), constructs canonical requests, and injects the Authorization header transparently. Your AI never handles raw crypto.
We've already built the connector for JD Cloud Infrastructure. Just plug in your AI agents and start using Vinkius.
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