Qiniu MCP Server Integration

What does “Qiniu” MCP Server do?

The Qiniu MCP Server is a Model Context Protocol (MCP) server built on top of Qiniu Cloud services, designed to bridge AI assistants and large language model clients with Qiniu’s cloud storage and intelligent multimedia services. By exposing storage and media processing capabilities via the MCP interface, it allows developers and AI-powered workflows to interact programmatically with Qiniu buckets, files, and CDN features directly from their AI tools. This integration enables tasks such as querying and managing cloud storage, uploading and downloading files, performing image transformations, and managing CDN caching. The Qiniu MCP Server streamlines the process of connecting external data sources and operational triggers to AI agents, enhancing development workflows and enabling seamless data-driven automation.

List of Prompts

No prompt templates are explicitly mentioned in the repository or documentation.

List of Resources

• Bucket List
  Exposes the list of all available Qiniu storage buckets accessible to the configured credentials.

• File List in Bucket
  Provides access to the list of files within a specified Qiniu bucket.

• File Content
  Allows reading the content of files stored in Qiniu buckets for use as LLM context.

• Download Link Resource
  Generates download links for files stored on Qiniu, enabling direct file access.

List of Tools

• Get Bucket List
  Retrieves the list of all storage buckets for the authenticated Qiniu account.

• Get File List in Bucket
  Fetches files stored within a selected bucket, supporting file management and selection.

• Upload File
  Supports uploading local files or content strings to a specified bucket.

• Read File Content
  Reads the contents of a specified file from a bucket for further processing.

• Generate Download Link
  Produces a public or signed download link for a file.

• Image Resize
  Performs image scaling operations as part of Qiniu’s intelligent media services.

• Image Rounded Corners
  Applies rounded corner effects to images stored in Qiniu.

• CDN Refresh by Link
  Refreshes CDN cache for a given file link to ensure updated content delivery.

• CDN Prefetch by Link
  Prefetches files to CDN nodes by link for faster access and lower latency.

Use Cases of this MCP Server

• Cloud Storage Management
  Enables automated listing, uploading, and reading of files in Qiniu buckets directly from AI agents or chatbots, streamlining data operations for developers.

• Media Processing Automation
  Facilitates image transformations like resizing or rounding corners as part of AI-driven content pipelines, reducing manual intervention.

• CDN Operations
  Allows AI workflows to trigger CDN cache refreshes or prefetches, ensuring content served to end-users is current and optimized.

• Workflow Integration
  Integrates Qiniu storage and media services into custom developer workflows, enhancing productivity and enabling new automation scenarios.

• Secure File Sharing
  Generates secure download links for files, enabling controlled and auditable file sharing from within AI or development environments.

How to set it up

Windsurf

No explicit Windsurf instructions are provided in the repository.

Claude

No explicit Claude instructions are provided in the repository.

Cursor

No explicit Cursor instructions are provided in the repository.

Cline

1. Install the Cline extension in VSCode (adds the Cline icon in the sidebar).
2. Configure the language model as needed.
3. Configure the Qiniu MCP Server:
   1. Click the Cline icon, select the MCP Server module.
   2. Under “installed”, click “Advanced MCP Settings” and add the following configuration:

{
  "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": "YOUR_BUCKET_A,YOUR_BUCKET_B"
      },
      "disabled": false
    }
  }
}

3. Click the toggle to connect the Qiniu MCP Server.

Securing API Keys

Store sensitive credentials like QINIU_ACCESS_KEY and QINIU_SECRET_KEY in environment variables using the env field in your configuration, as shown above.

Example:

"env": {
  "QINIU_ACCESS_KEY": "YOUR_ACCESS_KEY",
  "QINIU_SECRET_KEY": "YOUR_SECRET_KEY"
}

How to use this MCP inside flows

Using MCP in FlowHunt

To integrate MCP servers into your FlowHunt workflow, start by adding the MCP component to your flow and connecting it to your AI agent:

Click on the MCP component to open the configuration panel. In the system MCP configuration section, insert your MCP server details using this JSON format:

{
  "qiniu": {
    "transport": "streamable_http",
    "url": "https://yourmcpserver.example/pathtothemcp/url"
  }
}

Once configured, the AI agent is now able to use this MCP as a tool with access to all its functions and capabilities. Remember to change “qiniu” to whatever the actual name of your MCP server is and replace the URL with your own MCP server URL.

Overview

Based on the two tables, Qiniu MCP Server is well documented for core setup and feature coverage around Qiniu Cloud, but lacks explicit sampling/roots/prompt template support documentation. Its feature set is strong for storage and media, but more advanced MCP primitives are not detailed.

Our opinion

If your primary need is to integrate Qiniu Cloud file and media operations with AI assistant workflows, this MCP server is solid and covers all basic use cases with clear setup. For more advanced prompt/workflow or agentic features, documentation is thin. Score: 7/10.

MCP Score