Qiniu MCP Server

The Qiniu MCP Server bridges the gap between large‑model AI assistants and the rich ecosystem of Qiniu Cloud services. By exposing storage, media processing, and CDN operations through the Model Context Protocol (MCP), it lets developers weave cloud‑native workflows directly into AI conversations. Whether a user is querying bucket contents, uploading a new file, or applying on‑the‑fly image transformations, the server translates these requests into Qiniu API calls and returns structured results that an assistant can present or act upon.

At its core, the server offers three primary capability families:

• Storage – list buckets, enumerate files within a bucket, upload local or content‑based data, read file contents, and generate secure download URLs.
• Smart Multimedia – perform common image manipulations such as scaling and rounded‑corner cropping, enabling dynamic visual content generation without leaving the AI chat.
• CDN Management – refresh or prefetch assets by URL, ensuring that cached content stays current and delivers low‑latency responses to end users.

These operations are packaged as MCP resources and tools, so an assistant can call them with simple JSON payloads or embed them in prompts. The server runs on Python 3.12+ and can be launched locally or as an SSE service for web applications, making it flexible for both development and production environments.

Typical use cases include:

• AI‑powered file explorers – a user asks the assistant to list files in a bucket or read a document, and the assistant returns the data instantly.
• Dynamic media pipelines – an image is uploaded, resized to a thumbnail, and the CDN link is refreshed—all within one conversational turn.
• Content delivery optimization – before deploying a new version of an asset, the assistant can prefetch it across Qiniu’s CDN to reduce first‑request latency.

Because MCP treats cloud operations as first‑class citizens in the model’s context, developers can build end‑to‑end workflows that span data retrieval, transformation, and delivery without writing custom integration code. The server’s modular design also allows teams to extend it with additional business logic, making it a powerful foundation for AI‑driven cloud automation.