How to Use the JD Cloud Infrastructure MCP in LlamaIndex

Q: Can I search for specific VM metrics using JD Cloud Infrastructure and LlamaIndex?

Absolutely. By calling describemetricdata, you pull the latest stats into your index. You can then query for specific performance patterns or anomalies across your instances.

Q: How do I filter resources for LlamaIndex?

Use the allowedtools filter during client setup to restrict which tools contribute data to your index. This helps focus your knowledge base on the infrastructure you care about most.

Index your live JD Cloud Infrastructure status into LlamaIndex for semantic search and grounded RAG-based analysis.

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Connect JD Cloud Infrastructure MCP to LlamaIndex

Create your Vinkius account to connect JD Cloud Infrastructure to LlamaIndex and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

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Setup JD Cloud Infrastructure with LlamaIndex

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Query live infrastructure status in LlamaIndex

Transform raw API output from `list_vm_instances` into searchable knowledge. LlamaIndex indexes these results, allowing you to ask questions about your environment using natural language. Stop guessing the state of your fleet. You can ask your agent for a list of all running VMs, and it retrieves the current data directly from the cloud provider.

Grounded reasoning with JD Cloud Infrastructure

Use `describe_vm_instance` and `describe_cloud_disk` to pull real-time data into your RAG pipeline. This grounds your agent's answers in actual system facts rather than outdated documentation. Your responses are now backed by live telemetry. When you query resource configurations, the agent uses the latest data retrieved from your specific account.

Unified search for cloud resources

Combine `list_oss_buckets` and `list_rds_instances` data into a single vector index. This lets you search across all your storage and database assets through one interface. Finding specific resources becomes trivial. You no longer need to navigate through multiple dashboards to locate an instance or a bucket, as everything is indexed and searchable.

Setup guide

Set up JD Cloud Infrastructure MCP in LlamaIndex

Prerequisites

Python 3.10+ installed
llama-index-tools-mcp package
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai. The MCP tools package provides BasicMCPClient and McpToolSpec.
2

Connect with BasicMCPClient
Point BasicMCPClient to your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports SSE and Streamable HTTP transports.
3

Convert to LlamaIndex tools
Call mcp_tool_spec.to_tool_list_async() to convert all JD Cloud Infrastructure MCP tools into native FunctionTool objects that any LlamaIndex agent can use.
4

Run with any LLM
Create a FunctionAgent with the tools and your preferred LLM. Swap OpenAI for Anthropic, Gemini, or any LlamaIndex-supported provider.

agent.py

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

# Connect to the MCP
mcp_client = BasicMCPClient(
    "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)
mcp_tool_spec = McpToolSpec(client=mcp_client)

# Convert MCP tools to LlamaIndex tools
tools = await mcp_tool_spec.to_tool_list_async()

# Create and run the agent
agent = FunctionAgent(
    tools=tools,
    llm=OpenAI(model="gpt-4o"),
    system_prompt="You have access to JD Cloud Infrastructure tools.",
)
response = await agent.run("List recent JD Cloud Infrastructure data")

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Common questions about JD Cloud Infrastructure MCP in LlamaIndex

The MCP client retrieves raw data from the tools and passes it to the LlamaIndex engine. This data is then processed and stored as vectors for semantic search.

Absolutely. By calling `describe_metric_data`, you pull the latest stats into your index. You can then query for specific performance patterns or anomalies across your instances.

All data processing happens locally within your LlamaIndex pipeline. We ensure that your configuration details and cloud metadata remain protected during the indexing process.

You can refresh your index at any time by triggering a new tool call. This ensures your knowledge base stays aligned with the actual state of your cloud environment.

Use the allowed_tools filter during client setup to restrict which tools contribute data to your index. This helps focus your knowledge base on the infrastructure you care about most.

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