Sfcc Dev MCP
S

Sfcc Dev MCP

The SFCC Development MCP Server is an MCP protocol service that provides comprehensive support for Salesforce B2C Commerce Cloud development, including document query, log analysis, best practice guides, and system object definition functions, and supports AI assistant integration.
2.5 points
7.8K

What is the SFCC Development MCP Server?

This is an AI-assisted tool server designed specifically for Salesforce B2C Commerce Cloud (SFCC) developers. It provides intelligent access to SFCC development resources through the standard MCP protocol, including official document query, best practice guides, log analysis, and system object definition functions.

How to use the SFCC Development MCP Server?

You can start using it with simple configuration. There are two modes available: document query mode (no credentials required) and full mode (SFCC login credentials required). It can be integrated with AI assistants such as Claude to significantly improve development efficiency.

Applicable scenarios

Suitable for the entire SFCC development cycle: querying API documents during new feature development, analyzing logs during debugging, implementing best practices, and discovering system object attributes. It is particularly suitable for development tasks that require quick access to accurate SFCC information.

Main features

SFCC document query
Provides access to the complete SFCC API documentation, including functions such as querying class information, methods, and attributes, and supports fuzzy search.
Best practice guides
Built-in best practices for SFCC development, covering security, performance, and maintainability recommendations in key areas such as controllers, templates, and API hooks.
Log analysis
Provides real-time access to SFCC instance logs, supports viewing errors and warnings by category and pattern search, and helps quickly locate problems.
System object definition
Explore the complete structure of SFCC system objects (products, customers, orders, etc.), including custom attributes and site preferences.
SFRA documentation
Document query specifically provided for the Storefront Reference Architecture, including core modules such as controllers and request/response objects.
Advantages
Provides accurate SFCC official documentation and best practices, reducing AI hallucinations
Two usage modes meet different security requirements
Seamlessly integrates with mainstream AI assistants
Detailed system object exploration capabilities
Real-time log access accelerates the debugging process
Limitations
Full functionality requires SFCC instance access credentials
System object query requires additional configuration of an OAuth client
Initial configuration may require technical support

How to use

Select the usage mode
Decide to use the document query mode (no credentials required) or the full mode (requires dw.json configuration)
Configure the MCP client
Add the SFCC Development MCP server configuration to the MCP settings of the AI assistant
Prepare dw.json (full mode)
Create a dw.json file containing SFCC credentials
Integrate AI assistant instructions
Add sfcc-ai-instructions.md to the project directory and rename it to an instruction file that the AI assistant can recognize

Usage examples

Query API documentation
Need to know the available methods and attributes of the Product class during development
Debugging log analysis
Find relevant errors when order submission fails
Discover custom attributes
Find all custom attributes defined on the product

Frequently Asked Questions

Is there a fee for using this service?
What are the limitations of the document query mode?
How to ensure the security of my SFCC credentials?
Which AI assistants are supported?
What should I do if the system object query doesn't work?

Related resources

GitHub repository
Project source code and latest version
SFCC official documentation
Official developer documentation for Salesforce Commerce Cloud
MCP protocol specification
Official specification document for the Model Context Protocol
Configuration video tutorial
Video tutorial on the configuration and use of the SFCC MCP server

Installation

Copy the following command to your Client for configuration
{
  "mcpServers": {
    "sfcc-dev": {
      "command": "npx",
      "args": ["sfcc-dev-mcp"]
    }
  }
}

{
  "mcpServers": {
    "sfcc-dev": {
      "command": "npx",
      "args": ["sfcc-dev-mcp", "--dw-json", "/path/to/your/dw.json"]
    }
  }
}

{
  "mcpServers": {
    "sfcc-dev": {
      "command": "npx",
      "args": ["sfcc-dev-mcp"],
      "cwd": "/path/to/sfcc-dev-mcp"
    }
  }
}

{
  "mcpServers": {
    "sfcc-dev": {
      "command": "node",
      "args": ["/path/to/sfcc-dev-mcp/dist/main.js"]
    }
  }
}

{
  "mcpServers": {
    "sfcc-dev": {
      "type": "stdio",
      "command": "npx",
      "args": [
        "sfcc-dev-mcp",
        "--dw-json",
        "/path/to/your/dw.json"
      ]
    }
  }
}

{
  "mcpServers": {
    "sfcc-dev": {
      "type": "stdio", 
      "command": "npx",
      "args": [
        "sfcc-dev-mcp",
        "--dw-json",
        "/Users/username/Documents/Projects/my-sfcc-project/dw.json"
      ]
    }
  }
}

{
  "mcpServers": {
    "sfcc-dev": {
      "type": "stdio",
      "command": "npx",
      "args": ["sfcc-dev-mcp"],
      "cwd": "/path/to/your/sfcc/project"
    }
  }
}

{
    "mcpServers": {
      "sfcc-dev": {
        "command": "npx",
        "args": ["sfcc-dev-mcp", "--dw-json", "/path/to/dw.json", "--debug", "true"]
      }
    }
  }

{
  "mcpServers": {
    "sfcc-dev": {
      "command": "npx",
      "args": ["sfcc-dev-mcp", "--dw-json", "/path/to/dw.json", "--debug", "true"]
    }
  }
}
Note: Your key is sensitive information, do not share it with anyone.

Alternatives

V
Vestige
Vestige is an AI memory engine based on cognitive science. By implementing 29 neuroscience modules such as prediction error gating, FSRS - 6 spaced repetition, and memory dreaming, it provides long - term memory capabilities for AI. It includes a 3D visualization dashboard and 21 MCP tools, runs completely locally, and does not require the cloud.
Rust
6.4K
4.5 points
M
Moltbrain
MoltBrain is a long-term memory layer plugin designed for OpenClaw, MoltBook, and Claude Code, capable of automatically learning and recalling project context, providing intelligent search, observation recording, analysis statistics, and persistent storage functions.
TypeScript
5.1K
4.5 points
B
Bm.md
A feature-rich Markdown typesetting tool that supports multiple style themes and platform adaptation, providing real-time editing preview, image export, and API integration capabilities
TypeScript
5.4K
5 points
S
Security Detections MCP
Security Detections MCP is a server based on the Model Context Protocol that allows LLMs to query a unified security detection rule database covering Sigma, Splunk ESCU, Elastic, and KQL formats. The latest version 3.0 is upgraded to an autonomous detection engineering platform that can automatically extract TTPs from threat intelligence, analyze coverage gaps, generate SIEM-native format detection rules, run tests, and verify. The project includes over 71 tools, 11 pre-built workflow prompts, and a knowledge graph system, supporting multiple SIEM platforms.
TypeScript
6.5K
4 points
P
Paperbanana
Python
7.9K
5 points
B
Better Icons
An MCP server and CLI tool that provides search and retrieval of over 200,000 icons, supports more than 150 icon libraries, and helps AI assistants and developers quickly obtain and use icons.
TypeScript
6.6K
4.5 points
A
Assistant Ui
assistant - ui is an open - source TypeScript/React library for quickly building production - grade AI chat interfaces, providing composable UI components, streaming responses, accessibility, etc., and supporting multiple AI backends and models.
TypeScript
6.7K
5 points
A
Apify MCP Server
The Apify MCP Server is a tool based on the Model Context Protocol (MCP) that allows AI assistants to extract data from websites such as social media, search engines, and e-commerce through thousands of ready-to-use crawlers, scrapers, and automation tools (Apify Actors). It supports OAuth and Skyfire proxy payment and can be integrated into MCP clients such as Claude and VS Code through HTTPS endpoints or local stdio.
TypeScript
7.7K
5 points
G
Gitlab MCP Server
Certified
The GitLab MCP server is a project based on the Model Context Protocol that provides a comprehensive toolset for interacting with GitLab accounts, including code review, merge request management, CI/CD configuration, and other functions.
TypeScript
26.0K
4.3 points
D
Duckduckgo MCP Server
Certified
The DuckDuckGo Search MCP Server provides web search and content scraping services for LLMs such as Claude.
Python
73.6K
4.3 points
M
Markdownify MCP
Markdownify is a multi-functional file conversion service that supports converting multiple formats such as PDFs, images, audio, and web page content into Markdown format.
TypeScript
36.0K
5 points
N
Notion Api MCP
Certified
A Python-based MCP Server that provides advanced to-do list management and content organization functions through the Notion API, enabling seamless integration between AI models and Notion.
Python
21.7K
4.5 points
F
Figma Context MCP
Framelink Figma MCP Server is a server that provides access to Figma design data for AI programming tools (such as Cursor). By simplifying the Figma API response, it helps AI more accurately achieve one - click conversion from design to code.
TypeScript
65.4K
4.5 points
U
Unity
Certified
UnityMCP is a Unity editor plugin that implements the Model Context Protocol (MCP), providing seamless integration between Unity and AI assistants, including real - time state monitoring, remote command execution, and log functions.
C#
32.9K
5 points
G
Gmail MCP Server
A Gmail automatic authentication MCP server designed for Claude Desktop, supporting Gmail management through natural language interaction, including complete functions such as sending emails, label management, and batch operations.
TypeScript
22.2K
4.5 points
C
Context7
Context7 MCP is a service that provides real-time, version-specific documentation and code examples for AI programming assistants. It is directly integrated into prompts through the Model Context Protocol to solve the problem of LLMs using outdated information.
TypeScript
97.7K
4.7 points
AIBase
Zhiqi Future, Your AI Solution Think Tank
© 2026AIBase