How to Use the UPYUN Developer Platform MCP in LlamaIndex

Q: Can I use UPYUN Developer Platform and LlamaIndex together for auditing?

Yes. By indexing the output of getfileinfo and listdirectory, you create an audit trail that lets you ask complex questions about your infrastructure's history.

Index cloud infrastructure knowledge into RAG applications with LlamaIndex.

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Connect UPYUN Developer Platform MCP to LlamaIndex

Create your Vinkius account to connect UPYUN Developer Platform 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 UPYUN Developer Platform with LlamaIndex

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Search File System Status with MCP Server

LlamaIndex can index file system data using the `get_file_info` tool. Instead of just getting a JSON object, you build a searchable knowledge base around every asset's metadata. Query your past sessions to ask things like, 'What was the size and creation date of the assets in `/images/`?' and get answers grounded in actual API data.

Track Infrastructure Costs via LlamaIndex

Use `get_service_usage` to feed usage metrics into your index. This lets you query historical performance, answering questions like 'What was our CDN usage last month?' without hallucinating an answer. The RAG application combines live API data with internal documents for a unified search experience.

Build File Change Detection Indexes

You can create indices based on file operations. For example, you can index the results of `list_directory` before and after a set of changes. This lets your agent semantically answer: 'Which files were added or removed from `/images/` last week?' The ability to track these deltas makes it ideal for auditing cloud deployments.

Setup guide

Set up UPYUN Developer Platform 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 UPYUN Developer Platform 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 UPYUN Developer Platform tools.",
)
response = await agent.run("List recent UPYUN Developer Platform data")

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Common questions about UPYUN Developer Platform MCP in LlamaIndex

LlamaIndex ingests the results of file operations, like those from `create_text_file` or `delete_file`, and stores them in a vector store. This allows you to query past states semantically.

Yes. By indexing the output of `get_file_info` and `list_directory`, you create an audit trail that lets you ask complex questions about your infrastructure's history.

It provides operational data, including directory contents, file metadata, and billing metrics from the UPYUN bucket service.

The client supports passing multiple tool specifications. You can build a unified knowledge graph that spans data from several different MCP Servers.

This server touches file system metadata and usage statistics. The index stores the *content* of this data, which must be handled according to your application's policies.

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