MCP PhenoAge Clock Server

The MCP PhenoAge Clock server bridges the gap between clinical biochemistry and AI‑powered health analytics. It implements Dr. Morgan Levine’s PhenoAge algorithm, a validated biological aging metric that uses nine routine blood biomarkers plus chronological age to estimate an individual’s phenotypic age. This server gives developers a ready‑to‑use tool for integrating sophisticated aging assessments into conversational agents, health dashboards, or research pipelines without requiring deep expertise in biostatistics.

What Problem Does It Solve?

Traditional age is a blunt proxy for health status; two people of the same chronological age can have vastly different morbidity risks. The PhenoAge clock translates standard laboratory values—albumin, creatinine, fasting glucose, CRP, lymphocyte %, MCV, RDW, alkaline phosphatase, and WBC—into a single biological age estimate that correlates strongly with all‑cause mortality. By exposing this calculation as an MCP tool, the server allows AI assistants to provide personalized health insights, flag accelerated aging, and recommend preventive actions—all based on data that clinicians routinely collect.

Core Functionality & Value for Developers

• : Accepts raw biomarker inputs and returns a comprehensive report that includes the calculated PhenoAge, the age difference (biological minus chronological), a mortality risk score, and an interpretive summary. Developers can embed this tool in chatbots or clinical decision support systems to offer users an evidence‑based snapshot of their biological aging trajectory.
• : Supplies reference ranges and optimal values for each biomarker, enabling developers to validate user input or guide patients toward healthy thresholds before invoking the calculation.
• MCP Integration: The server is discovered via standard MCP discovery mechanisms, allowing any Claude‑compatible client to invoke the tools without custom connectors or API keys.

Use Cases & Real‑World Scenarios

• Personal Health Coaching: An AI coach can ask users for recent lab results, compute their PhenoAge, and suggest lifestyle changes (e.g., diet, exercise) that are likely to reduce biological age.
• Clinical Research: Researchers can batch‑process de‑identified lab datasets to explore correlations between phenotypic age and disease outcomes, leveraging the server’s deterministic algorithm.
• Preventive Medicine Platforms: Health portals can expose the tool as a self‑service feature, letting patients monitor their aging profile over time and receive alerts when their biological age surpasses safe thresholds.
• Insurance & Wellness Programs: Employers or insurers could use PhenoAge scores to design personalized wellness incentives that target individuals with accelerated aging.

Unique Advantages

• Evidence‑Based Formula: The server implements the original Levine et al. (2018) equation, ensuring that results align with peer‑reviewed research and clinical benchmarks.
• No External Dependencies: All calculations run locally within the MCP server, preserving data privacy and eliminating latency associated with third‑party APIs.
• Extensibility: Developers can easily augment the toolset (e.g., adding age‑adjusted risk calculators) or integrate additional biomarkers without modifying the core server code.
• Transparent Output: Alongside raw scores, the tool provides interpretive guidance and highlights units, fostering trust and clarity for end users.

In sum, the MCP PhenoAge Clock server equips AI assistants with a powerful, scientifically grounded aging metric that can be deployed in diverse health‑tech contexts—from personal coaching to large‑scale epidemiological studies—while maintaining ease of integration and user privacy.