How to Use the UPYUN Developer Platform MCP in LangChain

Q: How do I check CDN file status using UPYUN Developer Platform and LangChain?

You can call getfileinfo to retrieve metadata about any asset. This shows you the current state of your files, which is crucial before building a complex chain.

Q: Can I use UPYUN Developer Platform and LangChain to manage multiple folders?

Absolutely. The listdirectory tool lets you check contents for specific paths like /images/. You can pass the resulting directory structure into subsequent steps in your agent's chain.

Build complex, multi-step infrastructure agents using LangChain.

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Connect UPYUN Developer Platform MCP to LangChain

Create your Vinkius account to connect UPYUN Developer Platform to LangChain 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 LangChain

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Manage Cloud Files for LangChain Agents

The `list_directory` tool lets your agent map out the contents of any UPYUN directory. You check if a folder has everything you need, or find exactly where to start your file operations. It's perfect for multi-step workflows. Your agent can first run `get_file_info` on a target asset, then use that metadata to decide whether it needs to call `create_text_file` next.

Track Usage and Deploy Assets via MCP Server

Agents need visibility into cost. Use the `get_service_usage` tool to pull current UPYUN bucket service usage stats. This data feeds directly into your agent's reasoning chain, letting it decide if a deployment is financially viable. Need to clean up? The agent can check the status using `delete_file`, then call `list_directory` immediately after to confirm the file is gone.

Build File Creation Pipelines with LangChain

This MCP Server lets your AI client execute file lifecycle tasks. You can write a chain that first calls `create_text_file`, uploading new content, and then immediately uses `get_file_info` to confirm the upload was successful. It's robust for testing data pipelines. The agent handles the full sequence: create, verify metadata, and optionally, clean up with `delete_file` if the test fails.

Setup guide

Set up UPYUN Developer Platform MCP in LangChain

Prerequisites

Python 3.10+ installed
langchain-mcp-adapters + langgraph packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install langchain-mcp-adapters langgraph langchain-openai. The MCP adapters package converts MCP tools into native LangChain BaseTool objects.
2

Connect via HTTP transport
Use MultiServerMCPClient with "transport": "http" pointing to your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Create a ReAct agent
Pass the discovered tools to create_react_agent() from LangGraph. The agent automatically routes UPYUN Developer Platform tool calls through the MCP protocol.
4

Run with any LLM
Swap ChatOpenAI for ChatAnthropic, ChatGoogleGenerativeAI, or any LangChain-compatible model. The MCP tools work identically across all providers.

agent.py

from langchain_mcp_adapters.client import MultiServerMCPClient
from langgraph.prebuilt import create_react_agent
from langchain_openai import ChatOpenAI

async with MultiServerMCPClient({
    "upyun-developer-platform-mcp": {
        "transport": "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,
    )
    result = await agent.ainvoke({
        "messages": "List recent UPYUN Developer Platform transactions"
    })
    print(result["messages"][-1].content)

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Common questions about UPYUN Developer Platform MCP in LangChain

You can call `get_file_info` to retrieve metadata about any asset. This shows you the current state of your files, which is crucial before building a complex chain.

Absolutely. The `list_directory` tool lets you check contents for specific paths like `/images/`. You can pass the resulting directory structure into subsequent steps in your agent's chain.

The server exposes file system interactions, including file metadata, service usage reports via `get_service_usage`, and the ability to create or delete assets.

Yes. Because this is an MCP Server, your agent can use the output of these file operations—like a specific file path found via `list_directory`—as input for other database or API calls.

This server touches file system metadata and usage statistics. All interactions happen through your agent's secure token, keeping the data flow auditable.

Use it with your favorite AI tools

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