context-rot-detection
by Various
Context Rot Detection & Healing MCP Service — gives AI agents self-awareness about their cognitive state
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_DBLOG_FILEtoken_countsession_duration_minutestool_calls_countcontext_summaryagent_idclaude-opus-4-5claude-opus-4claude-sonnet-4gpt-4ogpt-4o-minio4-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
Pairs with
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