Cloud Storage MCP Server

The Cloud Storage MCP server bridges the gap between AI assistants and Google Cloud Storage, allowing developers to manage buckets and objects directly from conversational interfaces. By exposing a set of intuitive tools—such as listing buckets, inspecting files, and performing CRUD operations on objects—the server turns raw storage APIs into natural language actions. This eliminates the need for manual gcloud commands or custom scripts, enabling rapid iteration and automation in data‑driven workflows.

Developers benefit from a single entry point that consolidates multiple storage tasks into a coherent MCP interface. Instead of juggling separate SDKs or REST calls, an AI assistant can ask for a bucket list, upload a log file, or delete stale artifacts—all through straightforward prompts. The server’s design prioritizes clarity: each tool has a concise name (, , etc.) and the documentation explains its purpose in plain language, making it easy to onboard new team members or integrate into existing pipelines.

Key capabilities include:

• Bucket discovery: Quickly enumerate all buckets in one or more projects, supporting multi‑project environments via the environment variable.
• Metadata access: Retrieve detailed bucket or object metadata, such as storage class, lifecycle rules, and size.
• File management: Upload new files, download existing ones, or delete obsolete data with a single command.
• Project flexibility: The first project in the list is treated as default, while others can be targeted explicitly through tool arguments.

Typical use cases span from data science to DevOps. A data scientist might ask the assistant to pull a dataset from a bucket, process it, and store results back—all without leaving the chat. A CI/CD pipeline could trigger the assistant to clean up temporary build artifacts, ensuring storage costs remain controlled. Even compliance teams can request audit logs of bucket contents or confirm that sensitive files have been archived correctly.

Integration into AI workflows is straightforward: configure the MCP in the client’s config file, provide service‑account credentials, and expose the tools. The assistant can then invoke them via natural language, returning structured responses that the user can act upon or further analyze. This tight coupling between conversational AI and cloud storage empowers teams to focus on business logic rather than plumbing, accelerating delivery cycles and reducing operational overhead.