Qiniu MCP Server

Name: Qiniu MCP Server
Author: qiniu

@qiniu

代码仓库

关于 Qiniu MCP Server

暂无概览

#storage #image-processing

基本信息

分类

其他

许可证

MIT

运行时

python

传输方式

stdio

发布者

qiniu

提交者

yangsen

配置

使用下面的配置,将此服务器添加到你的 MCP 客户端。

{
  "mcpServers": {
    "qiniu": {
      "command": "uvx",
      "args": [
        "qiniu-mcp-server"
      ],
      "env": {
        "QINIU_ACCESS_KEY": "YOUR_ACCESS_KEY",
        "QINIU_SECRET_KEY": "YOUR_SECRET_KEY",
        "QINIU_REGION_NAME": "YOUR_REGION_NAME",
        "QINIU_ENDPOINT_URL": "YOUR_ENDPOINT_URL",
        "QINIU_BUCKETS": ""
      },
      "disabled": false
    }
  }
}

工具

qiniu mcp server version info.

Return the Bucket you configured based on the conditions.

List objects in Qiniu Cloud, list a part each time, you can set start_after to continue listing, when the number of listed objects is less than max_keys, it means that all files are listed. start_after can be the key of the last file in the previous listing.

Get an object contents from Qiniu Cloud bucket. In the GetObject request, specify the full key name for the object.

Upload text data to Qiniu bucket.

Upload a local file to Qiniu bucket.

Get the file download URL, and note that the Bucket where the file is located must be bound to a domain name. If using Qiniu Cloud test domain, HTTPS access will not be available, and users need to make adjustments for this themselves.

This function marks resources cached on CDN nodes as expired. When users access these resources again, the CDN nodes will fetch the latest version from the origin server and store them anew.

Newly added resources are proactively retrieved by the CDN and stored on its cache nodes in advance. Users simply submit the resource URLs, and the CDN automatically triggers the prefetch process.

Image scaling tool that resizes images based on a percentage and returns information about the scaled image. The information includes the object_url of the scaled image, which users can directly use for HTTP GET requests to retrieve the image content or open in a browser to view the file. The image must be stored in a Qiniu Cloud Bucket. Supported original image formats: psd, jpeg, png, gif, webp, tiff, bmp, avif, heic. Image width and height cannot exceed 30,000 pixels, and total pixels cannot exceed 150 million.

Image scaling tool that resizes images based on a specified width or height and returns information about the scaled image. The information includes the object_url of the scaled image, which users can directly use for HTTP GET requests to retrieve the image content or open in a browser to view the file. The image must be stored in a Qiniu Cloud Bucket. Supported original image formats: psd, jpeg, png, gif, webp, tiff, bmp, avif, heic. Image width and height cannot exceed 30,000 pixels, and total pixels cannot exceed 150 million.

Image rounded corner tool that processes images based on width, height, and corner radius, returning information about the processed image. If only radius_x or radius_y is set, the other parameter will be assigned the same value, meaning horizontal and vertical parameters will be identical. The information includes the object_url of the processed image, which users can directly use for HTTP GET requests to retrieve the image content or open in a browser to view the file. The image must be stored in a Qiniu Cloud Bucket. Supported original image formats: psd, jpeg, png, gif, webp, tiff, bmp, avif, heic. Image width and height cannot exceed 30,000 pixels, and total pixels cannot exceed 150 million. Corner radius supports pixels and percentages, but cannot be negative. Pixels are represented by numbers, e.g., 200 means 200px; percentages use !xp, e.g., !25p means 25%.

Retrieves basic image information, including image format, size, and color model.

概览

What is Qiniu MCP Server?

This MCP Server integrates with Qiniu Cloud products, enabling users to access Qiniu Cloud Storage, intelligent multimedia, and live streaming services within AI large model client contexts. It is built on the Model Context Protocol and is intended for developers who want to manage Qiniu resources through AI assistants.

How to use Qiniu MCP Server?

Install Python 3.12+ and the uv package manager. In an MCP client like Cline, configure the server by adding a JSON block (command: uvx, args: ["qiniu-mcp-server"]) and set environment variables for your Qiniu credentials (QINIU_ACCESS_KEY, QINIU_SECRET_KEY, QINIU_REGION_NAME, QINIU_ENDPOINT_URL, QINIU_BUCKETS). For live streaming only, you may substitute with QINIU_LIVE_API_KEY. Connect the server, then interact with the AI to run actions like listing buckets or managing live streams.

Key features of Qiniu MCP Server

List and manage storage buckets and files.
Upload local files and read file contents.
Generate file download links.
Resize and round-corner images.
Refresh and prefetch CDN by URL.
Manage live streaming spaces, streams, and domains.

Use cases of Qiniu MCP Server

Query bucket lists and file inventories via AI chat.
Upload a local file to a specific bucket using natural language.
Apply image transformations (resize, round corner) to stored images.
Invalidate or preload CDN resources with simple commands.
Create live streaming spaces, bind domains, and retrieve push/pull URLs.

FAQ from Qiniu MCP Server

What are the runtime requirements?

Python 3.12 or higher and the uv package manager are required. On macOS, brew install uv; on Linux/Windows, use the official install script.

How do I configure Qiniu credentials?

Set environment variables QINIU_ACCESS_KEY, QINIU_SECRET_KEY, QINIU_REGION_NAME, QINIU_ENDPOINT_URL, and optionally QINIU_BUCKETS (comma‑separated, max 20). For live streaming only, you can use QINIU_LIVE_API_KEY instead.

Why do I see "Error: spawn uvx ENOENT" in Claude?

The command field must contain the absolute path to uvx. Replace `"command": "

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