What signals make up the health score?
The health score is a weighted composite of token utilization quality (40%), retrieval accuracy (25%), tool-call burden (20%), and session length (15%).
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基本信息
Context Rot Detection & Healing — gives AI agents self-awareness about their cognitive state by analyzing token utilization, quality degradation, and session fatigue. Returns health scores (0-100), model-specific degradation curves for 15+ models, lost-in-the-middle risk scoring,
使用下面的配置,将此服务器添加到你的 MCP 客户端。
{
"mcpServers": {
"context-rot-detection": {
"command": "npx",
"args": [
"-y",
"context-rot-detection"
],
"env": {
"HEALTH_HISTORY_DB": "./health.db"
}
}
}
}Analyze your current context window health. Returns a health score (0-100), token utilization, estimated quality degradation, and recommendations for recovery. Call this periodically during long sessions or before critical decisions.
Retrieve health check history for an agent. Requires agent_id. Returns recent health checks and aggregate statistics.
Get service-wide utilization statistics: total calls, unique agents, model distribution, health score averages, and recent activity. Useful for operators monitoring service adoption and usage patterns.
Context Rot Detection is an MCP service that gives AI agents real-time visibility into their own cognitive health. It measures performance degradation as the context window fills up, providing a health score (0–100) based on token utilization, retrieval accuracy, and session fatigue. It is designed for developers building long-running production agent systems.
Install via npx (zero install) or npm global install. Configure as an MCP client tool for Claude Code or Claude Desktop, or run with Docker. Use the check_my_health tool periodically to analyze the current context window health, passing required token count and optional parameters.
The health score is a weighted composite of token utilization quality (40%), retrieval accuracy (25%), tool-call burden (20%), and session length (15%).
Curves are derived from empirical research (Chroma, Stanford, Redis) and are provided for 15+ curated models. Any HuggingFace model can be auto-resolved by fetching its config.json to extract context window size.
Health history is stored in a SQLite database configured via the `HEALTH_HISTORY_DB` environment variable (defaults to `:memory:`). The server caches HuggingFace model lookups in the same database.
Yes, for HuggingFace model auto-resolution it fetches `config.json` from the HuggingFace repo. If that fails, it falls back to conservative defaults. Local model detection does not require network.
The server uses stdio transport (standard MCP). There is no built-in authentication; it is intended to be invoked by MCP clients like Claude Desktop or Claude Code.

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