Context Rot Detection
MCP service that gives AI agents self-awareness about their cognitive state.
Every long-running AI agent suffers from context rot — measurable performance degradation as the context window fills up. Research from Chroma, Stanford ("lost-in-the-middle"), and Redis confirms this is the #1 practical failure mode in production agent systems.
An agent experiencing context rot doesn't know it's degrading — it just starts making worse decisions. This tool gives agents real-time visibility into their own cognitive health.
Features
- Health score (0–100) based on token utilization, retrieval accuracy, and session fatigue
- Model-specific degradation curves for 15+ curated models (Claude, GPT, Gemini, o-series)
- Auto-resolves any HuggingFace model — pass a repo ID like
meta-llama/Llama-3.1-70Band the context window is detected automatically, with results cached in SQLite - Lost-in-the-middle risk scoring based on Stanford research
- Tool-call burden and session fatigue analysis
- Actionable recovery recommendations — compact context, offload to memory, checkpoint, break into subtasks
- Per-agent health history tracking (SQLite)
- Service-wide utilization statistics
