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

pip install -r requirements.txt

Running the Server

python -m src.server

Usage with Claude Desktop

Add to your Claude Desktop configuration:

{
  "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:

python test_example.py

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