ToGMAL
Provides real-time, privacy-preserving analysis of LLM interactions to detect out-of-distribution behaviors and recommend safety interventions.
About
ToGMAL is a Model Context Protocol (MCP) server designed to enhance the safety and reliability of Large Language Model (LLM) interactions. It performs real-time, privacy-preserving analysis of LLM prompts and responses to identify and flag potentially harmful or problematic content, such as speculative theories, ungrounded medical advice, dangerous file operations, overly ambitious coding requests, and unsupported claims. By detecting these out-of-distribution behaviors, ToGMAL recommends appropriate safety interventions like step breakdown, human-in-the-loop oversight, or web searches, thereby helping prevent common LLM pitfalls and promoting more grounded AI interactions.
Key Features
- Taxonomy Building via crowdsourced evidence collection
- 0 GitHub stars
- Extensible for adding new detection patterns and categories
- Privacy-Preserving analysis (local, no external API calls)
- Low Latency heuristic-based detection for real-time analysis
- Intervention Recommendations (e.g., step breakdown, human-in-the-loop, web search)
Use Cases
- Detecting speculative theories or unsupported claims in LLM outputs before they are presented to users.
- Flagging dangerous or ungrounded instructions, such as medical advice or destructive file operations, generated by LLMs.
- Suggesting interventions for overly ambitious coding requests or complex tasks to ensure realistic and safe LLM assistance.
