AI-Powered Google Cloud Storage Tool Server
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This n8n workflow transforms a comprehensive set of Google Cloud Storage operations into AI-callable tools, allowing large language models to interact directly with your cloud storage. Empower your AI agent to effortlessly manage buckets and objects within GCS, streamlining cloud operations with intelligent automation.
About This Workflow
This powerful n8n workflow serves as an intelligent server, exposing a comprehensive suite of Google Cloud Storage (GCS) operations as callable tools for Large Language Models (LLMs). Utilizing the mcpTrigger node (Multi-Chain Prompt Trigger), it acts as a robust backend, enabling AI agents to programmatically create, delete, list, update, and retrieve both GCS buckets and objects. This setup dramatically simplifies the integration of AI into cloud infrastructure management, allowing for dynamic and context-aware automation of storage tasks without manual intervention. It offers a standardized interface for AI to perform essential cloud storage actions.
Key Features
- AI-Powered GCS Management: Exposes all major Google Cloud Storage operations as AI-callable tools.
- Comprehensive Bucket Control: Create, delete, get, list, and update metadata for GCS buckets.
- Granular Object Management: Handle objects by creating, deleting, retrieving data/metadata, listing, and updating metadata within buckets.
- Seamless LLM Integration: Designed with the
mcpTriggerfor direct, programmatic interaction with AI agents and prompts. - Automated Cloud Operations: Enable AI to autonomously perform routine or complex GCS tasks, enhancing efficiency and reducing manual overhead.
How To Use
- Set up the MCP Trigger: The 'Google Cloud Storage Tool MCP Server' node (
mcpTrigger) is the entry point for your AI. Ensure it's correctly configured to receive commands and expose the subsequent GCS nodes as tools to your LLM. - Authenticate Google Cloud Storage: For each
googleCloudStorageToolnode, provide the necessary Google Cloud credentials (e.g., Service Account Key) to securely access your GCS project. - Define Tool Parameters: Within each 'Google Cloud Storage Tool' node, specify the parameters (like
bucketName,objectName,projectID,data) that your AI will provide when calling the tool. Ensure the AI understands what input each tool expects. - Activate the Workflow: Once all nodes are configured and authenticated, activate the workflow. Your AI agent can now leverage these defined GCS operations as available tools via the MCP Server.
- Integrate with LLM: Connect the
mcpTrigger's webhook URL or API endpoint to your Large Language Model or AI framework, allowing it to discover and invoke these powerful GCS management tools in response to user prompts.
Apps Used
Workflow JSON
{
"id": "76a05b5c-1e1b-4748-88ec-4cc0811cf2ca",
"name": "AI-Powered Google Cloud Storage Tool Server",
"nodes": 15,
"category": "DevOps",
"status": "active",
"version": "1.0.0"
}Note: This is a sample preview. The full workflow JSON contains node configurations, credentials placeholders, and execution logic.
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ID: 76a05b5c-1e1b...
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