TL;DR

Implement fuzzy matching and normalization to automatically repair slightly misspelled or incorrectly formatted tool names in the scheduler.

Guidance

• Review the existing scheduler code to identify where tool names are processed and consider integrating a fuzzy matching library to handle slight misspellings.
• Implement a normalization function to convert tool names from kebab-case to snake_case, ensuring consistency in tool name formatting.
• Verify that the fuzzy matching and normalization logic correctly handles edge cases, such as tool names with special characters or varying capitalization.
• Consider adding logging or monitoring to track instances where the scheduler repairs tool names, helping to identify potential issues or areas for improvement.

Example

import re

def normalize_tool_name(tool_name):
    # Convert kebab-case to snake_case
    return re.sub('-+', '_', tool_name)

def fuzzy_match_tool_name(input_name, known_tools):
    # Simple fuzzy matching example, consider using a dedicated library
    for tool in known_tools:
        if input_name.lower() == tool.lower():
            return tool
    return None

Notes

The effectiveness of the fuzzy matching and normalization approach may depend on the specific requirements and constraints of the scheduler and the model used.

Recommendation

Apply workaround: Implementing fuzzy matching and normalization as described can help address the issue of slightly misspelled or incorrectly formatted tool names, improving the scheduler's robustness.