semvec/pypi-packages huggingface dataset:
loadept-mcp-filesystem 0.3.3
MCP Server for Filesystem Operations
Requires Python: >=3.13
Keywords: automation, filesystem, llm, mcp

# MCP File System Server

This MCP written in python interacts with the file system

# How to use
First you need to make sure you have uvx the python package and environment manager.
```sh
uv --version
```

If you don't have it, download it using:
```sh
# On macOS and Linux.
curl -LsSf https://astral.sh/uv/install.sh | sh
```
```sh
# On Windows.
powershell -ExecutionPolicy ByPass -c "irm https://astral.sh/uv/install.ps1 | iex"
```

Or visit the official website ofe uv at: [uv website](https://docs.astral.sh/uv/)

Then define the mcp Server in the different clients
### VSCode `$env:USERPROFILE\AppData\Roaming\Code\User\mcp.json`
```json
{
	"servers": {
		"loadept-mcp-filesystem": {
			"type": "stdio",
			"command": "uvx",
			"args": ["loadept-mcp-filesystem"],
			"env": {
				"BASE_PATH": "Path:\\To\\Base\\Dir"
			}
		}
	}
}
```

### Cursor AI `$env:USERPROFILE\.cursor\mcp.json`
```json
{
  "mcpServers": {
    "loadept-mcp-filesystem": {
      "command": "uvx",
      "args": ["loadept-mcp-filesystem"]
    }
  }
}
```

### Claude Desktop `$env:USERPROFILE\AppData\Roaming\Claude\claude_desktop_config.json`
```json
{
    "mcpServers": {
        "loadept-mcp-filesystem": {
            "command": "uvx",
            "args": ["loadept-mcp-filesystem"],
			"env": {
				"BASE_PATH": "Path:\\To\\Base\\Dir"
			}
        }
    }
}
```
temporal-replayer-adapter-python 1.0.3
Python Replayer Adapter for Temporal workflows with debugging capabilities
Requires Python: >=3.8
Keywords: temporal, workflow, debugging, replay, interceptor

# Python Replayer Adapter for Temporal

A Python adapter for debugging and replaying Temporal workflows with breakpoint support.

## Features

- **Workflow Debugging**: Comprehensive interceptors for debugging workflow execution
- **Breakpoint Support**: Set breakpoints for standalone mode or integrate with IDE debugger
- **Dual Modes**: Support for both standalone (JSON file) and IDE-integrated replay modes
- **Activity Debugging**: Interceptors for activity execution debugging

## Installation

```bash
# Install in development mode
pip install -e .

# Or install with development dependencies
pip install -e .[dev]
```

## Quick Start

### Standalone Mode

```python
from replayer_adapter_python import *

# Configure replay
set_replay_mode(ReplayMode.STANDALONE)
set_breakpoints([5, 15, 30])

# Replay workflow
opts = ReplayOptions(history_file_path="workflow_history.json")
replay(opts, YourWorkflowClass)
```

### IDE Integration Mode

```python
from replayer_adapter_python import *

# Set IDE mode (connects via WFDBG_HISTORY_PORT env var)
set_replay_mode(ReplayMode.IDE)

# Replay with IDE debugger
opts = ReplayOptions()
replay(opts, YourWorkflowClass)
```

## API Reference

### Core Functions

- `set_replay_mode(mode)`: Set replay mode (`ReplayMode.STANDALONE` or `ReplayMode.IDE`)
- `set_breakpoints(event_ids)`: Set breakpoints for standalone mode
- `replay(opts, workflow_class)`: Main replay function

### Classes

- `ReplayOptions`: Configuration for replay settings
- `RunnerWorkerInterceptor`: Main interceptor for workflow debugging

## Environment Variables

- `WFDBG_HISTORY_PORT`: Port for IDE debugger communication (default: 54578)

## Dependencies

- `temporalio>=1.15.0`
- `requests>=2.32.0`