JD Cloud Infrastructure MCP Server for LangChain 11 tools — connect in under 2 minutes
LangChain is the leading Python framework for composable LLM applications. Connect JD Cloud Infrastructure 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
Vinkius supports streamable HTTP and SSE.
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({
"jd-cloud-infrastructure": {
"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 JD Cloud Infrastructure, 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 JD Cloud Infrastructure MCP Server
Connect your AI agents directly to JD Cloud (京东云), the enterprise cloud infrastructure backing one of the world's largest e-commerce and supply chain platforms. This MCP provides 11 power tools spanning the full infrastructure lifecycle.
LangChain's ecosystem of 500+ components combines seamlessly with JD Cloud Infrastructure through native MCP adapters. Connect 11 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
- VM Lifecycle Management — List, inspect, start, stop, and reboot virtual machines through natural language
- Storage Operations — Enumerate and inspect cloud disks and Object Storage buckets
- Network Oversight — Query Elastic IP allocations and their association status
- Database Administration — List RDS instances with engine versions and connection status
- Performance Monitoring — Pull time-series CPU, network, and disk metrics for any resource
The JD Cloud Infrastructure MCP Server exposes 11 tools through the Vinkius. Connect it to LangChain 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 Infrastructure to LangChain via MCP
Follow these steps to integrate the JD Cloud Infrastructure MCP Server with LangChain.
Install dependencies
Run pip install langchain langchain-mcp-adapters langgraph langchain-openai
Replace the token
Replace [YOUR_TOKEN_HERE] with your Vinkius token
Run the agent
Save the code and run python agent.py
Explore tools
The agent discovers 11 tools from JD Cloud Infrastructure via MCP
Why Use LangChain with the JD Cloud Infrastructure MCP Server
LangChain provides unique advantages when paired with JD Cloud Infrastructure through the Model Context Protocol.
The largest ecosystem of integrations, chains, and agents. combine JD Cloud Infrastructure 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 JD Cloud Infrastructure queries for multi-turn workflows
JD Cloud Infrastructure + LangChain Use Cases
Practical scenarios where LangChain combined with the JD Cloud Infrastructure MCP Server delivers measurable value.
RAG with live data: combine JD Cloud Infrastructure tool results with vector store retrievals for answers grounded in both real-time and historical data
Autonomous research agents: LangChain agents query JD Cloud Infrastructure, synthesize findings, and generate comprehensive research reports
Multi-tool orchestration: chain JD Cloud Infrastructure tools with web scrapers, databases, and calculators in a single agent run
Production monitoring: use LangSmith to trace every JD Cloud Infrastructure tool call, measure latency, and optimize your agent's performance
JD Cloud Infrastructure MCP Tools for LangChain (11)
These 11 tools become available when you connect JD Cloud Infrastructure to LangChain via MCP:
describe_cloud_disk
Get detailed information about a specific cloud disk
describe_metric_data
Query monitoring metric data for a cloud resource
describe_vm_instance
Get detailed information about a specific VM instance
list_cloud_disks
List all cloud disk volumes in your region
list_elastic_ips
List all Elastic IP addresses in your region
list_oss_buckets
List all Object Storage Service buckets
list_rds_instances
List all RDS database instances in your region
list_vm_instances
List all virtual machine instances in your JD Cloud region
reboot_vm_instance
Reboot a VM instance
start_vm_instance
Start a stopped VM instance
stop_vm_instance
Stop a running VM instance
Example Prompts for JD Cloud Infrastructure in LangChain
Ready-to-use prompts you can give your LangChain agent to start working with JD Cloud Infrastructure immediately.
"List all my running virtual machines on JD Cloud."
"Show me the CPU usage for instance i-abc123 over the last hour."
Troubleshooting JD Cloud Infrastructure MCP Server with LangChain
Common issues when connecting JD Cloud Infrastructure to LangChain through the Vinkius, and how to resolve them.
MultiServerMCPClient not found
pip install langchain-mcp-adaptersJD Cloud Infrastructure + LangChain FAQ
Common questions about integrating JD Cloud Infrastructure 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?
Connect JD Cloud Infrastructure with your favorite client
Step-by-step setup guides for every MCP-compatible client and framework:
Anthropic's native desktop app for Claude with built-in MCP support.
AI-first code editor with integrated LLM-powered coding assistance.
GitHub Copilot in VS Code with Agent mode and MCP support.
Purpose-built IDE for agentic AI coding workflows.
Autonomous AI coding agent that runs inside VS Code.
Anthropic's agentic CLI for terminal-first development.
Python SDK for building production-grade OpenAI agent workflows.
Google's framework for building production AI agents.
Type-safe agent development for Python with first-class MCP support.
TypeScript toolkit for building AI-powered web applications.
TypeScript-native agent framework for modern web stacks.
Python framework for orchestrating collaborative AI agent crews.
Leading Python framework for composable LLM applications.
Data-aware AI agent framework for structured and unstructured sources.
Microsoft's framework for multi-agent collaborative conversations.
Connect JD Cloud Infrastructure to LangChain
Get your token, paste the configuration, and start using 11 tools in under 2 minutes. No API key management needed.