semvec/pypi-packages huggingface dataset:
mylabs-mcp.projectfour-expert 0.1.0
Project Four Model Context Protocol (MCP) server
Requires Python: >=3.13

# projectfour-expert

One Model Context Protocol (MCP) server

## Prerequisites

1. Install `uv` from [Astral](https://docs.astral.sh/uv/getting-started/installation/) or the [GitHub README](https://github.com/astral-sh/uv#installation)
2. Install Python using `uv python install 3.13`

## Installation

Install the MCP server:
```bash
uv tool install mylabs-mcp.projectfour-expert --force 
```

Add the server to your MCP client config (e.g. `~/.cursor-server/data/User/globalStorage/saoudrizwan.claude-dev/settings/cline_mcp_settings.json`):
```json
{
  "mcpServers": {
    "mylabs_mcp.projectfour_expert": {
      "command": "uvx",
      "args": [
        "--from",
        "mylabs-mcp-projectfour-expert",
        "projectfour-expert"
      ],
      "env": {
        "SHELL": "/usr/bin/zsh"
      },
      "disabled": false,
      "autoApprove": []
    }
  }
}
```

## Development

1. Set up the development environment:
```bash
uv sync --all-groups
```

2. Run the server:
```bash
uv run mylabs-mcp.projectfour-expert/server.py
```

3. Install pre-commit hooks:
```bash
GIT_CONFIG=/dev/null pre-commit install
```

## Running the Server

Run the server with default stdio transport:
```bash
uv run mylabs-mcp.projectfour-expert/server.py
```

Or with SSE transport on a specific port:
```bash
uvx projectfour-expert/server.py --sse --port 8888
```

## Customizing the Server

1. Edit `projectfour_expert/server.py` to implement your MCP tools and resources
2. Add dependencies to `pyproject.toml` with `uv add <dependency>`
3. Update tests and documentation as needed
4. Build the project: `uv build`
5. Push the changes: `git push`
6. Create a release: `uv run cz bump`
7. Tag the release: `git push --tags`