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context-rot-detection

by Various

Context Rot Detection & Healing MCP Service — gives AI agents self-awareness about their cognitive state

C

MCP

context-rot-detection

Added 1 June 2026

Overview

A service that detects degradation in an AI agent's context and attempts to restore it. It monitors the agent's cognitive state and provides self-awareness about context quality. The tool implements the Model Context Protocol for integration.

Best for

Best for
Developers building AI agents that need to maintain long-term context coherence

Use cases

  • Detecting when an AI agent loses track of conversation history
  • Restoring broken or stale context in agent interactions
  • Giving AI agents awareness of their own cognitive limitations

How to use

Install

npx context-rot-detection

Tools exposed

  • HEALTH_HISTORY_DB
  • LOG_FILE
  • token_count
  • session_duration_minutes
  • tool_calls_count
  • context_summary
  • agent_id
  • claude-opus-4-5
  • claude-opus-4
  • claude-sonnet-4
  • gpt-4o
  • gpt-4o-mini
  • o4-mini

Tested with

Claude Desktop, Claude Code

Notes

A service that detects degradation in an AI agent’s context and attempts to restore it. It monitors the agent’s cognitive state and provides self-awareness about context quality. The tool implements the Model Context Protocol for integration.

10 stars on GitHub. Last updated 2026-02-18. Licensed MIT.

Use cases

  • Detecting when an AI agent loses track of conversation history
  • Restoring broken or stale context in agent interactions
  • Giving AI agents awareness of their own cognitive limitations

Pros

  • Provides a mechanism for context self-monitoring
  • Helps maintain coherence in long conversations
  • Lightweight TypeScript implementation

Cons

  • Limited to 10 GitHub stars indicates early stage
  • May require significant integration effort for existing agents
  • Effectiveness depends on the quality of healing logic

Indexed from awesome-mcp-servers-punkpeye and enriched against its public facts.

Pros

  • Provides a mechanism for context self-monitoring
  • Helps maintain coherence in long conversations
  • Lightweight TypeScript implementation

Cons

  • Limited to 10 GitHub stars indicates early stage
  • May require significant integration effort for existing agents
  • Effectiveness depends on the quality of healing logic
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