Adobe Commerce Support MCP Server
This MCP server helps generate professional Adobe Commerce support responses from case findings.
Installation
- Dependencies are already installed ✅
- Server is ready to use ✅
Usage with Cursor
To use this MCP server with Cursor, add the following configuration to your Cursor settings:
Option 1: Add to Cursor Settings (Recommended)
- Open Cursor Settings (Cmd+,)
- Search for "MCP"
- Add this server configuration:
Option 2: Use the provided config file
The mcp_config.json
file in this directory contains the ready-to-use configuration.
How to Use
Option 1: Structured Content (Traditional)
- Create a
find.md
file with properly structured content:
- Use the
generate_support_reply
tool to create a professional customer response.
Option 2: Mixed Content (New - LLM Assisted)
- Create a
find.md
file with mixed/unstructured content (any format) - Use either:
categorize_mixed_content
tool first, then use LLM to process the categorization promptgenerate_support_reply
withauto_categorize=true
(default) for automatic handling
The server will automatically detect if your content needs categorization and provide LLM prompts to organize it properly.
Tools Available
- generate_support_reply: Generates a professional Adobe Commerce support response from case findings
find_file
: Input file (default: "find.md")resp_file
: Output file (default: "resp.md")auto_categorize
: Automatically handle mixed content (default: true)
- categorize_mixed_content: Creates LLM prompts to categorize mixed content into structured format
find_file
: Input file with mixed content (default: "find.md")categorized_file
: Output file with categorization prompt (default: "categorized.md")
Files
mcp_support_server.py
- Main MCP serverrequirements.txt
- Python dependenciesmcp_config.json
- Cursor configurationsetup.py
- Installation script
This server cannot be installed
local-only server
The server can only run on the client's local machine because it depends on local resources.
Generates professional Adobe Commerce support responses from case findings. Supports both structured and mixed content formats with automatic categorization capabilities.