Block 1 - Setup Instructions
- Type / Role
- n8n-nodes-base.stickyNote - stickyNote
- Config choices
- Version 1
This workflow is provided as-is. Please review and test before using in production.
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n8n-nodes-base.stickynote, @n8n/n8n-nodes-langchain.mcptrigger, n8n-nodes-base.httprequesttool
This workflow is cataloged by N8N Workflows and links back to its original n8n.io source page by David Ashby.
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Complete MCP server exposing 8 Metadata API operations to AI agents.
This workflow converts the Metadata API into an MCP-compatible interface for AI agents.
• MCP Trigger: Serves as your server endpoint for AI agent requests
• HTTP Request Nodes: Handle API calls to https://api.ebay.com{basePath}
• AI Expressions: Automatically populate parameters via $fromAI() placeholders
• Native Integration: Returns responses directly to the AI agent
• GET /country/{countryCode}/sales_tax_jurisdiction: Get Sales Tax Jurisdiction
• GET /marketplace/{marketplace_id}/get_automotive_parts_compatibility_policies: Get Get Automotive Parts Compatibility Policies • GET /marketplace/{marketplace_id}/get_extended_producer_responsibility_policies: Get Get Extended Producer Responsibility Policies • GET /marketplace/{marketplace_id}/get_hazardous_materials_labels: Get Get Hazardous Materials Labels • GET /marketplace/{marketplace_id}/get_item_condition_policies: Get Get Item Condition Policies • GET /marketplace/{marketplace_id}/get_listing_structure_policies: Get Get Listing Structure Policies • GET /marketplace/{marketplace_id}/get_negotiated_price_policies: Get Get Negotiated Price Policies • GET /marketplace/{marketplace_id}/get_return_policies: Get Get Return Policies
Parameter Handling: AI agents automatically provide values for: • Path parameters and identifiers • Query parameters and filters • Request body data • Headers and authentication
Response Format: Native Metadata API responses with full data structure
Error Handling: Built-in n8n HTTP request error management
Connect this MCP server to any AI agent or workflow:
• Claude Desktop: Add MCP server URL to configuration • Cursor: Add MCP server SSE URL to configuration • Custom AI Apps: Use MCP URL as tool endpoint • API Integration: Direct HTTP calls to MCP endpoints
• Zero Setup: No parameter mapping or configuration needed
• AI-Ready: Built-in $fromAI() expressions for all parameters
• Production Ready: Native n8n HTTP request handling and logging
• Extensible: Easily modify or add custom logic
> 🆓 Free for community use! Ready to deploy in under 2 minutes.
This catalog entry is organized from the workflow JSON. The node-level section below shows the executable blocks available for review before importing the template.
| Workflow | [eBay] metadata API MCP server |
|---|---|
| Complexity | intermediate |
| Nodes | 13 |
| Categories | Engineering, AI RAG |
| Author | David Ashby |
| Published | 02 Jul 2025 |
Use the JSON export at /data/workflows/5579/5579.json as the source template for this automation.
Open n8n, import the downloaded JSON, and review each node before activating the workflow.
Replace placeholder credentials, API keys, webhook URLs, account IDs, and environment-specific values with your own settings.
Run the workflow manually or in a staging workspace, inspect node output, and confirm downstream systems receive the expected data.
Enable the workflow only after testing, then monitor executions, errors, and rate limits during the first production runs.
Review imported nodes carefully before activation. This catalog entry is intended to help you inspect the workflow structure, understand required services, and find related templates faster.
Node names, credentials, schedules, webhook paths, and external service limits may need adjustment for your workspace.
Need help? Want access to this workflow + many more paid workflows + live Q&A sessions with a top verified n8n creator? [Join the community](https://www.skool.com/beyond nodes automation lab 2006/a...
Review the workflow JSON, configure any required credentials in n8n, and test the automation in a safe workspace before using it in production.
Yes. Use the block-by-block analysis and the downloadable JSON to inspect each node, then adjust credentials, prompts, schedules, filters, or destinations for your Engineering, AI RAG use case.