mcp-phenoage-clock

mcp-phenoage-clock

3.3

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An MCP server that calculates biological age using the Morgan Levine PhenoAge clock based on blood biomarkers.

MCP PhenoAge Clock Server

An MCP (Model Context Protocol) server that calculates biological age using the Morgan Levine PhenoAge clock based on blood biomarkers.

Overview

The PhenoAge clock is a biological aging measure developed by Dr. Morgan Levine and colleagues. It uses 9 blood biomarkers plus chronological age to estimate "phenotypic age" - a measure that captures morbidity and mortality risk better than chronological age alone.

Based on: Levine et al. (2018) "An epigenetic biomarker of aging for lifespan and healthspan" Aging (Albany NY). 2018;10(4):573-591

Installation

Via NPX (Recommended)

npx mcp-phenoage-clock

Via NPM

npm install -g mcp-phenoage-clock
mcp-phenoage-clock

From Source

git clone https://github.com/199-biotechnologies/mcp-phenoage-clock.git
cd mcp-phenoage-clock
npm install
npm run build
npm start

Usage with Claude Desktop

Add to your Claude Desktop configuration file:

macOS: ~/Library/Application Support/Claude/claude_desktop_config.json Windows: %APPDATA%\Claude\claude_desktop_config.json

{
  "mcpServers": {
    "phenoage-clock": {
      "command": "npx",
      "args": ["mcp-phenoage-clock"]
    }
  }
}

Available Tools

calculate_phenoage

Calculates your biological age using the PhenoAge formula.

Required biomarkers:

  • age: Chronological age (years)
  • albumin: Albumin (g/dL)
  • creatinine: Creatinine (mg/dL)
  • glucose: Glucose (mg/dL) - must be fasting
  • crp: C-reactive protein (mg/L)
  • lymphocytePercent: Lymphocyte percentage (%)
  • meanCellVolume: Mean cell volume/MCV (fL)
  • redCellDistWidth: Red cell distribution width/RDW (%)
  • alkalinePhosphatase: Alkaline phosphatase (U/L)
  • whiteBloodCellCount: White blood cell count (1000 cells/μL)

Returns:

  • PhenoAge (biological age)
  • Age difference (PhenoAge - chronological age)
  • Mortality score
  • Interpretation

get_biomarker_ranges

Returns reference ranges and optimal values for all biomarkers.

Example Usage in Claude

User: Calculate my PhenoAge with these values:
- Age: 45
- Albumin: 4.2 g/dL
- Creatinine: 0.9 mg/dL
- Glucose: 85 mg/dL
- CRP: 0.5 mg/L
- Lymphocyte %: 30
- MCV: 89 fL
- RDW: 12.5%
- Alkaline phosphatase: 65 U/L
- WBC: 6.2

Claude will use the calculate_phenoage tool and return your biological age.

Understanding Your Results

  • PhenoAge < Chronological Age: Your biological age is younger than your actual age, suggesting good health
  • PhenoAge ≈ Chronological Age: Normal aging pattern
  • PhenoAge > Chronological Age: Your biological age is older than your actual age, suggesting accelerated aging

Important Notes

  1. Fasting Glucose: The glucose measurement must be taken after fasting
  2. Units Matter: Ensure your lab results match the required units
  3. Medical Advice: This tool is for informational purposes only. Always consult healthcare providers for medical decisions
  4. Lab Variations: Different labs may use different reference ranges
  5. Formula Limitations: In rare cases with certain biomarker combinations, the mortality score may reach mathematical limits, resulting in clamped calculations. This is a limitation of the formula, not necessarily indicative of health status

Formula Details

The PhenoAge calculation uses the following formula:

xb = -19.907 - 0.0336×albumin + 0.0095×creatinine + 0.1953×glucose 
     + 0.0954×ln(CRP) - 0.0120×lymphocyte% + 0.0268×MCV 
     + 0.3306×RDW + 0.00188×alkaline_phosphatase + 0.0554×WBC + 0.0804×age

mortality_score = 1 - exp(-exp(xb) × (exp(120×γ) - 1) / γ)
where γ = 0.0076927

PhenoAge = 141.50225 + ln(-0.00553 × ln(1 - mortality_score)) / 0.090165

Development

# Install dependencies
npm install

# Build
npm run build

# Run in development
npm run dev

License

MIT

Contributing

Contributions welcome! Please submit PRs to the GitHub repository.

References

  • Levine ME, Lu AT, Quach A, et al. An epigenetic biomarker of aging for lifespan and healthspan. Aging (Albany NY). 2018;10(4):573-591.
  • Original paper supplementary materials: Table S6 contains the full coefficient values