MCP-Mem0

An implementation of the Model Context Protocol (MCP) server integrated with CumulusCI for providing AI agents with Salesforce development capabilities.

This server enables AI agents to interact with CumulusCI commands without developers needing to remember complex CLI syntax.

Overview

This project demonstrates how to build an MCP server that enables AI agents to execute CumulusCI operations for Salesforce development workflows. It serves as a foundation for creating more comprehensive CCI integrations.

The implementation follows the best practices laid out by Anthropic for building MCP servers, allowing seamless integration with any MCP-compatible client.

Features

The server currently provides one essential CCI operation:

1. create_scratch_org: Create a new scratch org using the CCI dev_org flow

Prerequisites

• Python 3.12+
• Access to a Salesforce org (Dev Hub for scratch orgs)
• Docker if running the MCP server as a container (recommended)

Environment Setup

The MCP server provides CCI installation checking and setup instructions. When you encounter CCI command not found errors, use the check_cci_installation tool which will guide you through:

# Check if CCI is installed
cci version

# Install CCI if not present
pipx install cumulusci-plus-azure-devops

# Upgrade CCI if needed
pipx install cumulusci-plus-azure-devops --force

Installation

Using uv

1. Install uv if you don't have it:

   pip install uv
   

2. Clone this repository:

   git clone <your-repo-url>
   cd sfcore-th-dev
   

3. Install dependencies:

   uv pip install -e .
   

4. Ensure CumulusCI is installed and configured:

   pip install cumulusci
   cci org connect <your-dev-hub>
   

Using Docker (Recommended)

1. Build the Docker image:

   docker build -t ghcr.io/jorgesolebur/mcp-sfcore-th-dev:latest --build-arg PORT=8050 .
   

2. Push to GitHub Container Registry:

   docker push ghcr.io/jorgesolebur/mcp-sfcore-th-dev:latest
   

   Note: You'll need to authenticate with GitHub Container Registry first:

   echo $GITHUB_TOKEN | docker login ghcr.io -u jorgesolebur --password-stdin
   

Configuration

The following environment variables can be configured in your .env file:

Variable	Description	Default
TRANSPORT	Transport protocol (sse or stdio)	sse
HOST	Host to bind to when using SSE transport	0.0.0.0
PORT	Port to listen on when using SSE transport	8050

The server relies on CumulusCI being properly configured on the system where it runs.

Running the Server

Using uv

SSE Transport

# Set TRANSPORT=sse in .env then:
uv run src/main.py

The MCP server will run as an API endpoint that you can connect to with the config shown below.

Stdio Transport

With stdio, the MCP client itself can spin up the MCP server, so nothing to run at this point.

Using Docker

SSE Transport

docker run -d -p 8050:8050 ghcr.io/jorgesolebur/mcp-sfcore-th-dev:latest

The MCP server will run as an API endpoint within the container.

Stdio Transport

With stdio, the MCP client itself can spin up the MCP server container, so nothing to run at this point.

Integration with MCP Clients

SSE Configuration

Once you have the server running with SSE transport, you can connect to it using this configuration:

{
  "mcpServers": {
    "sfcore-th-dev": {
      "transport": "sse",
      "url": "http://localhost:8050/sse"
    }
  }
}

Note for Windsurf users: Use serverUrl instead of url in your configuration:

{
  "mcpServers": {
    "sfcore-th-dev": {
      "transport": "sse",
      "serverUrl": "http://localhost:8050/sse"
    }
  }
}

Note for n8n users: Use host.docker.internal instead of localhost since n8n has to reach outside of its own container to the host machine:

So the full URL in the MCP node would be: http://host.docker.internal:8050/sse

Make sure to update the port if you are using a value other than the default 8050.

Python with Stdio Configuration

Add this server to your MCP configuration for Claude Desktop, Windsurf, or any other MCP client:

{
  "mcpServers": {
    "sfcore-th-dev": {
      "command": "your/path/to/sfcore-th-dev/.venv/Scripts/python.exe",
      "args": ["your/path/to/sfcore-th-dev/src/main.py"],
      "env": {
        "TRANSPORT": "stdio"
      }
    }
  }
}

Docker with Stdio Configuration

{
  "mcpServers": {
    "sfcore-th-dev": {
      "command": "docker",
      "args": ["run", "--rm", "-i", 
               "-e", "TRANSPORT", 
               "ghcr.io/jorgesolebur/mcp-sfcore-th-dev:latest"],
      "env": {
        "TRANSPORT": "stdio"
      }
    }
  }
}

Extending the Server

This template provides a foundation for building more comprehensive CCI integrations. To add new CCI tools:

1. Create a new @mcp.tool() method
2. Use the get_cci_command_instructions() utility function for consistent behavior
3. Example:

   @mcp.tool()
   async def deploy_to_org(org_name: str = "dev") -> str:
       command = f"cci flow run deploy --org {org_name}"
       purpose = f"Deploy to org '{org_name}'"
       return get_cci_command_instructions(command, purpose)
   

This ensures all tools have consistent command execution and error handling.

Available Tools

Environment Setup

• check_cci_installation: Checks if CumulusCI is installed and provides installation/upgrade instructions

Scratch Org Management

• create_dev_scratch_org: Creates a development scratch org using cci flow run dev_org --org <org_name>
• create_feature_scratch_org: Creates a feature/QA scratch org using cci flow run ci_feature_2gp --org <org_name>
• create_beta_scratch_org: Creates a beta/regression scratch org using cci flow run regression_org --org <org_name>
• list_orgs: Lists all connected CumulusCI orgs using cci org list
• open_org: Opens the specified org in a browser using cci org browser --org <org_name>

Development Operations

• run_tests: Runs Apex tests in a specified org using cci task run run_all_tests_locally --org <org_name>
• retrieve_changes: Retrieves metadata changes from the specified org using cci task run retrieve_changes --org <org_name>
• deploy: Deploys local metadata to the specified org using cci task run deploy --org <org_name>

Generic CCI Task Handler

• run_generic_cci_task: Handles any CCI task that doesn't have a dedicated tool following a 3-step approach:
  1. Checks if the task exists using cci task list
  2. Gets task information and parameters using cci task info or cci task run --help
  3. Runs the task with appropriate parameters after collecting required values from the user

All CCI tools provide setup guidance if needed and follow consistent error handling patterns.

MCP Resources

The server provides framework-specific documentation through MCP resources. These resources give agents contextual information about development practices and standards.

Available Resources

Access resources using the URI pattern: framework://<framework-name>

• framework://salesforce-triggers: Comprehensive guidelines for developing Apex triggers

  • Trigger framework architecture
  • Handler pattern implementation
  • Best practices and anti-patterns
  • Testing strategies
  • Performance considerations

• framework://salesforce-logging: Logging standards and best practices

  • Custom logger implementation
  • Log levels and usage guidelines
  • Performance considerations
  • Production logging strategies
  • Security and privacy considerations

• framework://salesforce-cache-manager: Platform Cache management framework for performance optimization

  • Three cache types: Organization, Session, and Transaction
  • Declarative configuration with custom metadata
  • Usage examples and best practices
  • Performance monitoring and debugging
  • Security considerations for cached data

Using Resources

Agents can request framework documentation when needed:

{
  "method": "resources/read",
  "params": {
    "uri": "framework://salesforce-triggers"
  }
}

This provides on-demand access to framework-specific guidance without cluttering tool descriptions.

Future Enhancements

Consider adding:

• Additional framework resources (LWC, Aura, Flows)
• More CCI operation tools
• Integration with CI/CD pipelines
• Advanced testing workflows