Precision Medicine MCP Platform

Dedicated to PatientOne -- a dear friend who passed from High-Grade Serous Ovarian Carcinoma in 2025.

This platform automates multi-modal data processing for clinical decision support — all results require clinician review before any clinical action.

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The Problem

Standard oncology workup (BRCA1/2, HRD panel, tumor genomic panel) generates no immunotherapy or investigational hypotheses. For preventive health, standard lipid panels and population genetic screens miss key risk factors. Manual multi-modal analysis across genomics, spatial transcriptomics, and clinical data is clinically impractical -- the platform automates it.

The Platform

A multi-server MCP architecture orchestrated by AI (Claude + Gemini) executes a 5-stage pipeline:

flowchart LR
    A["1 Data<br/>Acquisition"] --> B["2 Spatial<br/>Deconvolution"]
    B --> C["3 Target<br/>Profiling"]
    C --> D["4 Causal<br/>Inference"]
    D --> E["5 Report"]

    subgraph servers [" "]
        direction TB
        S1["EHR · GEO · TCGA"]
        S2["Spatial · DeepCell · CIBERSORTx"]
        S3["OpenTargets · Neoantigen"]
        S4["Perturbation · Quantum"]
        S5["Patient Report"]
    end

    A --- S1
    B --- S2
    C --- S3
    D --- S4
    E --- S5

    AI(["AI Orchestrator<br/>Claude + Gemini"]) -.-> A
    AI -.-> B
    AI -.-> C
    AI -.-> D
    AI -.-> E

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Architecture at a glance

                  +--------------------------------------+
                  |           CLIENT LAYER               |
                  |  Claude Desktop / Hospital EHR       |
                  |  Adapter / Research Notebook         |
                  +----------------+-----------------+
                                   |
                         MCP (FastMCP >= 2.13)
                                   |
   +---------------------------------------------------------------+
   |                                                               |
   |  DATA ACQUISITION      ANALYSIS & INFERENCE      REPORTING   |
   |                                                               |
   |  mockepic              spatialtools              patient-     |
   |  epic                  multiomics                report       |
   |  geodownload           perturbation                           |
   |  mocktcga              quantum-fidelity                       |
   |  genomic-results       opentargets                            |
   |  fgbio                 neoantigen                             |
   |                        cibersortx                             |
   |                        openimagedata                          |
   |                        deepcell                               |
   |                        cell-classify                          |
   |                        cardiometabolic                        |
   +---------------------------------------------------------------+

All tools accessible via natural language. Every AI result requires clinician APPROVE/REVISE/REJECT. HIPAA-compliant. Current server and tool counts: Server Registry.

The Results

The platform surfaces clinically actionable findings that standard workup cannot reach — 6 investigational hypotheses across 2 cancer types plus 3 preventive health evidence gaps, validated across three independent use cases:

Use Case	Patient	Key Finding Missed by Standard Workup
HGSOC (Stage IV)	PAT001	3 investigational paths: neoantigen vaccine (RMPEAAPPV IC50 7.8 nM), NNMT/CAF inhibition, convergent checkpoint blockade
ER+ Breast Cancer	PAT002	3 investigational hypotheses: inavolisib over alpelisib (PIK3CA H1047R, 2024 FDA approval), MYC-driven triple therapy, YSAPLSSSL neoepitope vaccine + CAF depletion + anti-PD-1 — zero disease-specific code changes
Preventive Cardiovascular	PAT003	Intermediate CVD risk (Reynolds 14.3%) with 3 high-priority gaps missed by standard lipid panel AND population genetic screen: Lp(a), APOE genotype, CAC score

The same 19-server architecture runs all three. No disease-specific code changes between use cases.

Validated results — PAT001 (HGSOC)

Metric	Value	Source server
HRD score	54	mcp-genomic-results
TMB (POLE-corrected)	47.3 mut/Mb	mcp-genomic-results
Top neoantigen IC50 (RMPEAAPPV)	7.8 nM	mcp-neoantigen
Spatial spot count	900	mcp-spatialtools
Moran's I (global)	-0.0033	mcp-spatialtools
Deconvolution: tumor	56 cells	mcp-cibersortx
Deconvolution: endothelial	44 cells	mcp-cibersortx
Deconvolution: macrophages	43 cells	mcp-cibersortx
Deconvolution: fibroblasts	41 cells	mcp-cibersortx
Deconvolution: CD8+ T cells	30 cells	mcp-cibersortx

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Try It

# Clone and explore
git clone https://github.com/lynnlangit/precision-medicine-mcp.git
cd precision-medicine-mcp

# Run tests for any server (DRY_RUN mode, no external deps needed)
cd servers/mcp-multiomics && uv run pytest -v

# Or use Claude Code to explore interactively
claude

All servers default to DRY_RUN mode (mock responses, no API keys needed) for quick validation. Set *_DRY_RUN=false to use synthetic patient data for end-to-end testing.

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Learn More

Audience	Start Here
Getting Started	Installation Guide
Funders	Executive Summary
Hospitals	Hospital Guide
Developers	Architecture
Researchers	Researcher Guide
Educators	Educator Guide
All docs	Documentation Index

Video: 5-minute demo | Paper: Why MCP for Healthcare | External connectors: Setup guide

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Apache 2.0 | Python 3.11+ | FastMCP >= 2.13 | uv for package management