MCP-Mem0

by jorgesolebur

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About

Integrate long-term memory into AI agents using Mem0.

Details

Author
jorgesolebur
Categories
Developer Tools, AI

Setup

Install MCP-Mem0 in your MCP client (Claude Desktop, Cursor, Windsurf, and others).

Repository: https://github.com/jorgesolebur/mcp-cci

Follow the installation instructions in the repository README, then restart your MCP client.

SFCore TH Dev: CumulusCI Integration for AI Agents

An implementation of theModel Context Protocol (MCP)server integrated withCumulusCIfor 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.

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.

The server currently provides one essential CCI operation:
- create_scratch_org: Create a new scratch org using the CCI dev_org flow

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

The MCP server provides CCI installation checking and setup instructions. When you encounter CCI command not found errors, use thecheck_cci_installationtool 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
git clone <your-repo-url> cd sfcore-th-dev

Ensure CumulusCI is installed and configured:

pip install cumulusci cci org connect <your-dev-hub>
docker build -t ghcr.io/jorgesolebur/mcp-sfcore-th-dev:latest --build-arg PORT=8050 .
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

The following environment variables can be configured in your.envfile:

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

# 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.

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

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.

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

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: UseserverUrlinstead ofurlin 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.

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

This template provides a foundation for building more comprehensive CCI integrations. To add new CCI tools:
- Create a new@mcp.tool()method
- Use theget_cci_command_instructions()utility function for consistent behavior
- 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.

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

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

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

- run_generic_cci_task: Handles any CCI task that doesn't have a dedicated tool following a 3-step approach:
- Checks if the task exists usingcci task list
- Gets task information and parameters usingcci task infoorcci task run --help
- 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.

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

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

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.

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

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