semvec/pypi-packages huggingface dataset:
stdexcept 0.1.0
Terminal Raise Error Tools
License: MIT
Requires Python: >=3.7
Keywords: output, stdout, stderr, suppress, mute, silence

<span id="top"></span>
# Python Module-StdExcept
| Chapter | Link |
| --- | ---|
| Top | [Click to jump](#top) |
| What's this | [Click to jump](#what) |
| For Syntax | [Click to jump](#syntax) |
| Update record | [Click to jump](#update) |
| Developers | [Click to jump](#dev) |

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## What's this
> This is a Python module.Expanded Python's error-throwing syntax.

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## For Syntax
If your code can raise a runtime-error,code is:
> import stdexcept
> raise stdexcept.runtime_error('404 Not Found')

Or:
> from stdexcept.errors import runtime_error
> runtime_error('404 Not Found')

For examples:
> from stdexcept.errors import runtime_error,logic_error,domain_error,out_of_range,invalid_arguments,length_error,range_error

If you can custom a error,code is:
> import stdexcept
> stdexcept.custom_error('Network error','502 Error')

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## Update record
### Version 0.1
> This is first version.None.

<span id="dev"></span>
## Developers
### RSC-E Studio [GitHub Page](https://www.github.com/RSC-E)
> SYSTEM-WIN12-ZDY

Support with:
Mail:rscestudio114514@gmail.com \
QQ:3298296537
ml-training-init-mcp 0.2.2
MCP server for ML training script initialization with file locking
License: MIT
Requires Python: >=3.10

# ML Training Init MCP Server

A Model Context Protocol (MCP) server that implements a sequential thinking pattern for ML training script generation with managed file constraints.

## Key Feature: Managed File System (1 Training + 2 Configs)

**IMPORTANT**: This server enforces a managed file constraint system:
- **1 Training Script**: The main ML training file that will be executed
- **Up to 2 Config Files**: Optional configuration files (YAML, JSON, .env, etc.)
- ALL operations work only with these managed files
- Prevents agents from creating unnecessary files when stuck
- Clearly identifies ML training workflows with [ML TRAINING WORKFLOW] tags

## Installation

```bash
pip install -r requirements.txt
```

## Running the Server

```bash
python -m src.server
```

## Usage with Claude Desktop

Add to your Claude Desktop configuration:

```json
{
  "mcpServers": {
    "ml-training-init": {
      "command": "python",
      "args": ["-m", "src.server"],
      "cwd": "/path/to/ml-training-init-mcp"
    }
  }
}
```

## Available Tools

### 1. `initialize_training_file`
- Creates the main training script file
- Takes: file_name, content, reference
- Returns: file_path, file_type, managed_files status
- AI agents use this for ML training workflows

### 2. `create_config_file`
- Creates configuration files (max 2 allowed)
- Supports: YAML, JSON, TOML, .env, .ini, etc.
- Takes: file_name, content, config_type
- Returns: file_path, file_type, managed_files status

### 3. `get_managed_files`
- Lists all managed files (training + configs)
- Shows file paths and names
- Returns: training file info, config files list

### 4. `get_file_content`
- Get content of a specific managed file
- Takes: file_name
- Returns: file_path, content, file_type

### 5. `get_current_file`
- Quick access to training file content
- Returns: file_path, content

### 6. `monitor_and_fix`
- Fix errors in any managed file
- Takes: error_trace, file_name (optional)
- Auto-parses common Python/ML errors

## Testing

Run the test example:
```bash
python test_example.py
```

This will show you how to interact with the MCP server through Claude.