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.

JD Cloud Infrastructure MCP Server for LlamaIndex 11 tools — connect in under 2 minutes

Name: JD Cloud Infrastructure
Availability: InStock
Author: Vinkius

docs.llamaindex.ai/en/stable/module_guides/tools/mcp

Built by Vinkius GDPR 11 Tools Framework

LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add JD Cloud Infrastructure as an MCP tool provider through Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.

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 llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI

async def main():
    # Your Vinkius token. get it at cloud.vinkius.com
    mcp_client = BasicMCPClient("https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
    mcp_tool_spec = McpToolSpec(client=mcp_client)
    tools = await mcp_tool_spec.to_tool_list_async()

    agent = FunctionAgent(
        tools=tools,
        llm=OpenAI(model="gpt-4o"),
        system_prompt=(
            "You are an assistant with access to JD Cloud Infrastructure. "
            "You have 11 tools available."
        ),
    )

    response = await agent.run(
        "What tools are available in JD Cloud Infrastructure?"
    )
    print(response)

asyncio.run(main())

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* 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.

LlamaIndex agents combine JD Cloud Infrastructure tool responses with indexed documents for comprehensive, grounded answers. Connect 11 tools through Vinkius and query live data alongside vector stores and SQL databases in a single turn. ideal for hybrid search, data enrichment, and analytical workflows.

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 LlamaIndex 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 LlamaIndex via MCP

Follow these steps to integrate the JD Cloud Infrastructure MCP Server with LlamaIndex.

Install dependencies

Run pip install llama-index-tools-mcp llama-index-llms-openai

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 11 tools from JD Cloud Infrastructure

Why Use LlamaIndex with the JD Cloud Infrastructure MCP Server

LlamaIndex provides unique advantages when paired with JD Cloud Infrastructure through the Model Context Protocol.

Data-first architecture: LlamaIndex agents combine JD Cloud Infrastructure tool responses with indexed documents for comprehensive, grounded answers

Query pipeline framework lets you chain JD Cloud Infrastructure tool calls with transformations, filters, and re-rankers in a typed pipeline

Multi-source reasoning: agents can query JD Cloud Infrastructure, a vector store, and a SQL database in a single turn and synthesize results

Observability integrations show exactly what JD Cloud Infrastructure tools were called, what data was returned, and how it influenced the final answer

JD Cloud Infrastructure + LlamaIndex Use Cases

Practical scenarios where LlamaIndex combined with the JD Cloud Infrastructure MCP Server delivers measurable value.

Hybrid search: combine JD Cloud Infrastructure real-time data with embedded document indexes for answers that are both current and comprehensive

Data enrichment: query JD Cloud Infrastructure to augment indexed data with live information before generating user-facing responses

Knowledge base agents: build agents that maintain and update knowledge bases by periodically querying JD Cloud Infrastructure for fresh data

Analytical workflows: chain JD Cloud Infrastructure queries with LlamaIndex's data connectors to build multi-source analytical reports

JD Cloud Infrastructure MCP Tools for LlamaIndex (11)

These 11 tools become available when you connect JD Cloud Infrastructure to LlamaIndex 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 LlamaIndex

Ready-to-use prompts you can give your LlamaIndex 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 LlamaIndex

Common issues when connecting JD Cloud Infrastructure to LlamaIndex through the Vinkius, and how to resolve them.

BasicMCPClient not found

Install: pip install llama-index-tools-mcp

JD Cloud Infrastructure + LlamaIndex FAQ

Common questions about integrating JD Cloud Infrastructure MCP Server with LlamaIndex.

How does LlamaIndex connect to MCP servers?

Use the MCP client adapter to create a connection. LlamaIndex discovers all tools and wraps them as query engine tools compatible with any LlamaIndex agent.

Can I combine MCP tools with vector stores?

Yes. LlamaIndex agents can query JD Cloud Infrastructure tools and vector store indexes in the same turn, combining real-time and embedded data for grounded responses.