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OpenPGx — The Open Standard for AI-Readable Pharmacogenomics

OpenPGx is an open, AI-readable standard for pharmacogenomic data. It defines how genetic variants, drug responses, disease risks, and traits should be structured so that any AI system can understand and reason about them.

The standard is delivered today through an MCP Server — plug it into Claude, Cursor, or any MCP-compatible AI and start asking questions about your DNA.

"Can I take Ozempic?"       → Checks GLP1R variants against your genotype
"How about Vyvanse?"        → Cross-references CYP2D6 + COMT studies
"What's my Alzheimer risk?" → 19 disease conditions analyzed with odds ratios

Privacy-first: your data never leaves your computer. No cloud, no account, no tracking.


Why OpenPGx?

Pharmacogenomic data is trapped in formats that AI can't use. FHIR is verbose and hospital-centric. PharmCAT is great but not designed for AI consumption. Research papers are unstructured text.

OpenPGx is different:

  • AI-readable — structured JSON that any LLM can parse and reason about

  • Study-driven — every interpretation traces back to a published study with PMID/DOI

  • Open source — add a study by dropping a JSON file and opening a PR

  • FHIR-compatible — not a replacement, but an intelligence layer that exports to FHIR resources

The OpenPGx Study Format

One JSON file = one gene-drug study. This is the atomic unit of pharmacogenomic knowledge in OpenPGx:

{
  "gene": "ALDH2",
  "category": "drug_metabolism",
  "gene_description": "Alcohol metabolism, nitroglycerin bioactivation",
  "drugs": ["nitroglycerin", "ethanol"],
  "source": {
    "pmid": "16395407",
    "doi": "10.1172/JCI26564",
    "source_type": "pubmed",
    "title": "ALDH2 Glu504Lys polymorphism and nitroglycerin efficacy",
    "journal": "Journal of Clinical Investigation",
    "year": 2006,
    "cohort_size": 986,
    "url": "https://pubmed.ncbi.nlm.nih.gov/16395407/",
    "finding": "ALDH2*2 carriers show reduced nitroglycerin bioactivation."
  },
  "snps": [
    {
      "rsid": "rs671",
      "risk_allele": "A",
      "reference_allele": "G",
      "interpretations": {
        "AA": { "phenotype": "ALDH2 Deficient", "effect": "Nitroglycerin ineffective", "severity": "severe" },
        "AG": { "phenotype": "Reduced Activity", "effect": "33-40% less efficacy", "severity": "moderate" },
        "GG": { "phenotype": "Normal", "effect": "Standard response", "severity": "info" }
      }
    }
  ],
  "evidence_level": "established"
}

Want to contribute a new drug-gene study? Create a file like this in data/pgx/studies/ and open a PR. No code changes needed.


Related MCP server: gwas-mcp

Install in 30 seconds

Claude Desktop

Add to your claude_desktop_config.json:

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

{
  "mcpServers": {
    "openpgx": {
      "command": "npx",
      "args": ["-y", "openpgx"]
    }
  }
}

Restart Claude Desktop. Done.

Claude Code (CLI)

claude mcp add openpgx -- npx -y openpgx

Cursor

Open Settings > MCP > Add new MCP Server:

{
  "mcpServers": {
    "openpgx": {
      "command": "npx",
      "args": ["-y", "openpgx"]
    }
  }
}

Windsurf / Cline / Any MCP client

Same configuration. OpenPGx uses stdio transport — any MCP-compatible client works:

npx openpgx

Remote server (no install)

Don't want to install anything? Use the hosted server directly:

{
  "mcpServers": {
    "openpgx": {
      "type": "streamable-http",
      "url": "https://mcp.openpgx.ai/mcp"
    }
  }
}

This connects to our remote MCP server via Streamable HTTP — same 9 tools, zero local setup. Your genome is parsed server-side and stored temporarily in memory (30-minute session TTL).

Privacy note: For maximum privacy, prefer the npx install — everything stays on your machine. The remote server processes your data but does not store it permanently.


What can you ask?

Medications (Pharmacogenomics)

"Upload my genome"                    → parse your raw DNA file
"Can I take Ozempic?"                 → check semaglutide + GLP1R
"What about Venvanse?"                → brand names work (60+ brands mapped)
"Is modafinil right for me?"          → checks COMT + CYP2C19 interactions
"Compare sertraline vs escitalopram"  → head-to-head comparison
"Weight loss medications"             → search by category
"antidepressivo"                      → Portuguese works too
"ozmpic"                              → typos are auto-corrected

Disease Risks

"What's my cancer risk?"              → check specific conditions
"Full risk report"                    → all 19 conditions analyzed
"Do I have the Alzheimer gene?"       → APOE status
"Am I at risk for blood clots?"       → Factor V Leiden check

Traits

"Trait report"                        → all 25+ traits
"Am I lactose intolerant?"            → lactose persistence check
"Am I a morning person?"              → chronotype analysis

Supplements

"Supplement protocol"                 → MTHFR, COMT, VDR, BCMO1, FUT2, CBS
"Should I take methylfolate?"         → based on your MTHFR status

What's in the knowledge base

118 studies · 109 genes · 219 drugs · 19 disease risks · 31 traits — all backed by published research with PMID/DOI.

Genes with study data (109)

Every gene is backed by at least one published study with interpretations per genotype. The authoritative list is the set of gene fields in data/pgx/studies/*.json; the table below groups major clinical themes (some genes appear in more than one theme in the data).

Category

Genes (representative)

Drug Metabolism

CYP2D6, CYP2C19, CYP2C9, CYP2B6, CYP3A4, CYP3A5, CYP1A2, CYP2E1, ALDH2, BCHE, CES1, FAAH, NAT2, UGT1A1, UGT1A4, UGT1A9, GSTP1, ACE, ADRB1, ADRB2, AGTR1, HMGCR, PCSK9

Drug Targets & Response

VKORC1, DRD2, DRD3, DRD4, HTR1A, HTR2A, HTR2C, GABRA2, GABRA6, GLP1R, GRIK4, SCN1A, SCN9A, MC1R, RYR1, TPMT, NUDT15, DPYD, FKBP5, ESR1, SHBG, COMT, OPRM1, TCF7L2, CLOCK

ADHD / Stimulants

ADRA2A, SLC6A2, SLC6A3, SLC6A4, SNAP25

Drug Transport

SLCO1B1, ABCB1, ABCG2, SLC22A1, SLC22A2

Immune / HLA

HLA-B, HLA-A, HLA-C, HLA-DQB1, IL23R

GLP-1 / Incretin

GLP1R, GIPR, MC4R, PCSK1

Methylation & Vitamins

MTHFR, COMT, VDR, BCMO1, FUT2, CBS, DHCR7, SLC23A1, GC, TCN2, NBPF3

Lipid Metabolism

PNPLA3, TM6SF2, APOE, APOA5, APOB, LDLR, LIPC, LPL, SORT1, PPARG

Energy Balance / Obesity

FTO, MC4R, GHSR, BDNF, PCSK1, TMEM18, FABP2, LYPLAL1

Glucose / Insulin

GCKR, SLC2A2, PPM1K, MTNR1B, IL6, C11ORF65

Circadian Rhythm

PER2, CRY2, NR1D1, CLOCK

Coagulation

F2, F5

Cannabinoid / Related

CNR1, AKT1, CYP2C9

Salt Sensitivity

GRK4, CLCNKA

127 Medications & Compounds

Therapeutic Area

Medications

Cardiology & Anticoagulation

warfarin, clopidogrel, rivaroxaban, apixaban, dabigatran, enoxaparin, heparin, nitroglycerin, isosorbide dinitrate, digoxin, amlodipine, losartan, hydrochlorothiazide, chlorthalidone, furosemide

Statins & Lipid-Lowering

simvastatin, atorvastatin, rosuvastatin, pravastatin, ezetimibe, fenofibrate, gemfibrozil, niacin, evolocumab, alirocumab, mipomersen

Psychiatry & Neurology

clozapine, olanzapine, risperidone, haloperidol, aripiprazole, quetiapine, fluoxetine, paroxetine, escitalopram, citalopram, venlafaxine, bupropion, carbamazepine, oxcarbazepine, eslicarbazepine, phenytoin, donepezil, memantine, lecanemab

ADHD & Wakefulness

modafinil, armodafinil, methylphenidate, lisdexamfetamine, amphetamine, atomoxetine, caffeine

Pain & Opioids

codeine, tramadol, fentanyl, morphine, methadone

Oncology

capecitabine, fluorouracil, cisplatin, carboplatin, oxaliplatin, paclitaxel, topotecan, methotrexate, tamoxifen

Immunosuppressants

tacrolimus, azathioprine, mercaptopurine, thioguanine, sulfasalazine

Metabolic & GLP-1

semaglutide, liraglutide, tirzepatide, dulaglutide, setmelanotide, metformin, pioglitazone, rosiglitazone, insulin, orlistat, empagliflozin, dapagliflozin

Infectious Disease

abacavir, efavirenz

Supplements & Vitamins

omega-3/fish oil/EPA/DHA, vitamin D (cholecalciferol, ergocalciferol, calcitriol), vitamin C (ascorbic acid), folic acid, methylfolate, methylcobalamin, beta-carotene, retinol, resveratrol, nicotinamide riboside, NMN, melatonin, pyridoxine

Other

allopurinol, febuxostat, theophylline, tizanidine, cannabidiol, ethanol, disulfiram, celecoxib, ibuprofen, naproxen

19 Disease Risk Conditions

Category

Conditions

Oncology

Prostate Cancer, Breast Cancer (BRCA), Colorectal Cancer, Melanoma

Cardiovascular

Coronary Artery Disease, Atrial Fibrillation

Neurological

Alzheimer's Disease, Parkinson's Disease

Metabolic

Type 2 Diabetes, Hereditary Hemochromatosis, Gout

Autoimmune

Celiac Disease, Psoriasis, Rheumatoid Arthritis, Lupus (SLE)

Hematological

Venous Thromboembolism (Factor V Leiden)

Musculoskeletal

Osteoporosis

Respiratory

Asthma

Ophthalmological

Age-Related Macular Degeneration

31 Traits

Caffeine metabolism, alcohol flush, lactose tolerance, muscle composition, chronotype, eye color, and more.

Clinical Conditions Covered by Gene Studies

Beyond disease risk SNPs, the gene studies provide pharmacogenomic guidance across these clinical areas:

Area

What OpenPGx covers

Blood Clotting

Factor V Leiden (F5), Prothrombin mutation (F2), warfarin sensitivity (VKORC1, CYP2C9)

Drug Hypersensitivity

HLA-B57:01 → abacavir, HLA-A31:01 → carbamazepine DRESS, HLA-B*15:02 → SJS/TEN

Statin Myopathy

SLCO1B1 poor transport → simvastatin muscle toxicity

Chemotherapy Toxicity

DPYD deficiency → fluorouracil/capecitabine, TPMT/NUDT15 → thiopurines, GSTP1 → platinum agents

Opioid Response

CYP2D6 poor/ultrarapid → codeine, tramadol, fentanyl dosing

Antipsychotic Side Effects

HTR2C → weight gain risk, DRD2 → efficacy, CYP2D6 → metabolism

Obesity & Weight Loss

FTO, MC4R, BDNF, PCSK1, GHSR, CLOCK, SIRT1, LYPLAL1 — 9 genes affecting appetite, metabolism, fat distribution

Cardiovascular Lipids

APOE, APOA5, APOB, LDLR, LIPC, LPL, SORT1, PNPLA3, TM6SF2 — LDL, HDL, triglycerides, fatty liver

Diabetes & Glucose

GCKR, SLC2A2, MTNR1B, PPM1K, PPARG, IL6 — insulin secretion, glucose sensing, BCAA metabolism

Salt Sensitivity & Hypertension

GRK4, CLCNKA — sodium handling, diuretic response

Circadian & Sleep

PER2, CRY2, NR1D1, CLOCK — chronotype, shift work risk, melatonin response

Vitamin Metabolism

MTHFR → folate, DHCR7 → vitamin D synthesis, BCMO1 → beta-carotene, VDR → vitamin D receptor, SLC23A1 → vitamin C, FUT2 → B12

Omega-3 & Fat Absorption

FADS1 → DHA/EPA conversion, FABP2 → dietary fat absorption

GLP-1 Drug Response

GLP1R, GIPR → semaglutide/tirzepatide efficacy prediction

9 MCP Tools

Tool

Description

upload_genome

Parse raw DNA data (23andMe, Genera)

check_medication

Smart drug lookup — brand names, generics, typos, categories

full_pgx_report

Complete pharmacogenomic report

supplement_protocol

Supplement optimization based on gene variants

compare_medications

Head-to-head drug comparison

check_risk

Check genetic risk for a specific disease

full_risk_report

Comprehensive disease risk report

trait_report

All genetic traits analysis

full_report

Everything combined: medications + risks + traits


Contributing studies

OpenPGx is designed for open source contribution. Adding a new drug-gene study requires zero code changes — just a JSON file.

How to contribute

  1. Find a pharmacogenomic study (PubMed, CPIC guidelines, PharmGKB)

  2. Create a JSON file in data/pgx/studies/ following the naming pattern: {gene}_{year}_{slug}.json

  3. Open a PR

The system automatically:

  • Creates the gene if it doesn't exist in the catalog

  • Registers all rsIDs for DNA parsing

  • Builds the drug-to-gene index

  • Makes the interpretations available in all reports

Study file template

{
  "gene": "GENE_SYMBOL",
  "category": "drug_metabolism",
  "gene_description": "What this gene does",
  "drugs": ["generic_drug_name"],
  "source": {
    "pmid": "12345678",
    "doi": "10.xxxx/xxxxx",
    "source_type": "pubmed",
    "title": "Study title",
    "journal": "Journal name",
    "year": 2024,
    "cohort_size": 1000,
    "url": "https://pubmed.ncbi.nlm.nih.gov/12345678/",
    "finding": "One-line summary of the key finding"
  },
  "snps": [
    {
      "rsid": "rs12345",
      "risk_allele": "A",
      "reference_allele": "G",
      "interpretations": {
        "AA": { "phenotype": "Poor Metabolizer", "effect": "...", "recommendation": "...", "severity": "severe" },
        "AG": { "phenotype": "Intermediate", "effect": "...", "recommendation": "...", "severity": "moderate" },
        "GG": { "phenotype": "Normal", "effect": "...", "recommendation": "...", "severity": "info" }
      }
    }
  ],
  "evidence_level": "established"
}

Full schema: openpgx.schema.json (see $defs/study_contribution)


The OpenPGx Output Standard (v0.4.0)

Patient-facing OpenPGx files are a single JSON object. The only required top-level field is openpgx_version (must be "0.4.0"). Everything else follows openpgx.schema.json.

Top-level structure:

Field

Required

Role

openpgx_version

yes

Specification version; const 0.4.0

metadata

no

generated_at, generator, sources; optional last_updated

provenance

no

Audit trail: version, previous_version_hash, changelog[] (date, reason, optional description, affected_sections)

patient

no

Profile only (no genotypes): id, raw_data_source, raw_data_format, extraction_date, ancestry

observations

no

Raw measurements: each item has gene, rsid, genotype; optional chromosome, position, diplotype, activity_score

medications

no

Per-drug blocks: drug (name, class, optional brand_names, atc_code, drugbank_id), pgx_associations[], optional interactions[], optional dosing, plus parsed_at, parse_source, confidence (score, evidence_level, …)

risks

no

Disease risk: condition, category (enum incl. oncology, cardiovascular, …), overall_risk, risk_snps[], evidence, actionable, recommendation, studies; optional icd10, polygenic_score, lifetime_risk

traits

no

trait, category, snps[], your_phenotype, description, evidence, practical_advice, studies

fhir_mapping

no

Hints for FHIR: patient_reference, service_request_id, resource_mappings, terminology_systems

Minimal valid skeleton (only the required field):

{
  "openpgx_version": "0.4.0"
}

Example with the main optional sections (names shortened; see schema for full $defs):

{
  "openpgx_version": "0.4.0",
  "metadata": {
    "generated_at": "2026-04-12T12:00:00Z",
    "generator": "openpgx-mcp/1.x",
    "sources": ["CPIC", "PharmGKB"]
  },
  "patient": {
    "raw_data_source": "23andMe",
    "ancestry": "european"
  },
  "observations": [
    {
      "gene": "CYP2C19",
      "rsid": "rs4244285",
      "genotype": "AG"
    }
  ],
  "medications": [
    {
      "drug": { "name": "clopidogrel", "class": "antiplatelet" },
      "pgx_associations": [
        {
          "gene": "CYP2C19",
          "rsid": "rs4244285",
          "effect": "Reduced active metabolite formation",
          "evidence": { "level": "established", "sources": [] },
          "clinical_recommendation": "Consider alternative antiplatelet per guideline"
        }
      ],
      "parsed_at": "2026-04-12T12:00:00Z",
      "parse_source": "cpic_guideline",
      "confidence": { "score": 0.9, "evidence_level": "established" }
    }
  ],
  "fhir_mapping": {
    "resource_mappings": {
      "observations": "Observation (code: LOINC 81247-9 'Master HL7 genetic variant reporting panel')",
      "medications": "DiagnosticReport (code: LOINC 51969-4 'Genetic analysis report') + MolecularSequence",
      "risks": "RiskAssessment (method: LOINC 75321-0 'Clinical genomics report')",
      "traits": "Observation (category: genomics)"
    }
  }
}

Full schema: openpgx.schema.json


Architecture

Raw DNA file (23andMe .txt / Genera .csv)
        |
        v
+------------------+
|  Parser           | --> Extracts relevant SNPs from 600K+ raw data
|  (local, private) |
+------------------+
        |
        v
+------------------+
|  Study Catalog    | --> 118 studies x 109 genes x 219 drugs
|  (data/pgx/       |     Auto-creates gene definitions
|   studies/*.json)  |     Builds drug-gene index at runtime
+------------------+
        |
        v
+------------------+
|  MCP Tools        | --> 9 tools for AI interaction
|  (server-core.ts) |     Falls back to AI web search when needed
+------------------+
        |
        v
    AI Assistant (Claude, Cursor, ChatGPT, etc.)

All processing happens locally. No data is sent to any server.


Supported raw data formats

  • 23andMe (.txt) — fully supported

  • Genera (.csv) — fully supported

  • AncestryDNA — coming soon

  • VCF — coming soon


Smart Drug Resolution

OpenPGx resolves drug names through 6 layers:

  1. Brand > Generic — "Ozempic" > semaglutide (60+ brands including Venvanse, Rivotril, Marevan, Provigil, Stavigile)

  2. Generic exact match — "semaglutide" > found

  3. Fuzzy brand match — "ozmpic" > Ozempic > semaglutide

  4. Fuzzy generic match — "sertralina" > sertraline

  5. Category search — "weight loss", "antidepressant", "antidepressivo" > list of drugs

  6. Semantic search — pre-computed TF-IDF embeddings (289KB)


Development

git clone https://github.com/open-pgx/open-pgx.git
cd open-pgx/mcp-server
npm install
npm run build
npm start

Project structure

mcp-server/
├── src/
│   ├── index.ts            # Entry point (stdio transport)
│   ├── server-core.ts      # MCP tools (11 tools)
│   ├── pgx-catalog.ts      # Study loader, gene index, phenotype inference
│   ├── parsers.ts           # 23andMe & Genera raw data parsers
│   ├── pharmacogenes.ts     # Barrel re-exports
│   ├── drug-resolver.ts     # 6-layer smart drug resolution
│   ├── risk-catalog.ts      # Disease risk definitions
│   ├── trait-catalog.ts     # Trait definitions
│   └── types.ts             # Shared TypeScript interfaces
├── data/
│   ├── pgx/studies/         # 118 study files (the knowledge base)
│   ├── risks/               # Disease risk condition definitions
│   ├── traits/              # Trait definitions
│   ├── drugs_embeddings.json # TF-IDF vectors for semantic search
│   └── tfidf_vocab.json     # TF-IDF vocabulary
├── genomes/                  # Safe gitignored dir for your DNA files
└── package.json

Privacy & Security

  • Your genetic data never leaves your computer

  • No cloud upload, no account, no tracking

  • Pre-commit hook prevents accidentally committing DNA files

  • .gitignore blocks all genetic file patterns

  • The code is open source — audit it yourself


License

  • Specification (OpenPGx format): Apache License 2.0

  • Code (MCP server, parsers): MIT License


Disclaimer

OpenPGx is for educational and research purposes only. It does not replace professional medical advice. Always consult your physician before making decisions about medications or supplements. Genetic risk is probabilistic, not deterministic. Evidence levels and source publications are provided for every association.


OpenPGx — The open standard for AI-readable pharmacogenomics.

openpgx.ai · GitHub · npm

Available Tools

16 tools
check_medicationCheck MedicationA
Read-onlyIdempotent

Check how your genes affect a medication. Supports brand names ("Ozempic"), generic names ("semaglutide"), typos ("ozmpic"), and natural language ("weight loss injection", "antidepressivo").

Inputs: drug_name (required).

check_riskCheck Disease RiskA
Read-onlyIdempotent

Check your genetic risk for a specific disease or condition. Supports: cancer, alzheimer's, diabetes, heart disease, celiac, hemochromatosis, macular degeneration, blood clots, and more.

Inputs: condition (required).

compare_medicationsCompare MedicationsA
Read-onlyIdempotent

Compare two medications head-to-head based on your genetic profile. Supports brand names.

Inputs: drug1 (required), drug2 (required).

diagnostic_allComplete Diagnostic ReportA
Read-onlyIdempotent

Generate the most comprehensive genomic report available. Runs ALL modules at once: pharmacogenomics (all genes + clinical context), disease risk assessment (all 19 conditions with study citations and PMIDs), and genetic traits (all 30+ traits with studies). Includes odds ratios, evidence levels, risk allele counts, and full bibliography. This is the 'one-click full diagnostic' — the user does NOT need to ask condition by condition.

Inputs: no named parameters.

full_pgx_reportFull PGx ReportA
Read-onlyIdempotent

Generate a complete pharmacogenomic report with all your genes, phenotypes, and affected medication categories.

Inputs: no named parameters.

full_reportFull Genomic ReportA
Read-onlyIdempotent

Generate a complete genomic intelligence report covering all three modules: medications (PGx), disease risks, and traits. The most comprehensive analysis available.

Inputs: no named parameters.

full_risk_reportFull Risk ReportA
Read-onlyIdempotent

Generate a comprehensive genetic disease risk report covering all analyzed conditions: cancer, cardiovascular, neurological, metabolic, autoimmune, and more.

Inputs: no named parameters.

get_upload_commandGet Upload CommandA
Read-onlyIdempotent

Returns a ready-to-run curl command that uploads a genome file directly to the OpenPGx server. Execute this command in your code execution sandbox or tell the user to run it in their terminal. The command sends the file WITHOUT reading it into context. After running it, use load_profile with the returned code.

Inputs: file_path (required).

load_profileLoad Uploaded ProfileA
Idempotent

RECOMMENDED for remote server. Load a genome profile using a code (e.g., pgx-a7b3c) from the /upload endpoint. The user ran a curl command (from get_upload_command) in their terminal or sandbox and received this code. This is the primary way to load genome data on the remote server without reading file contents into context.

Inputs: code (required).

save_drug_researchSave Drug ResearchB

Save pharmacogenomic research from web search as a local study. Validates the data, caches it, and returns the medication check result.

Inputs: study_json (required).

search_drug_pgxResearch Drug PGx OnlineA
Read-onlyIdempotent

When no local study data exists for a drug, this tool returns a structured research prompt. Use YOUR web search capability to find pharmacogenomic data, then call save_drug_research with the results.

Inputs: drug_name (required).

supplement_protocolSupplement ProtocolA
Read-onlyIdempotent

Analyze genes related to supplement metabolism: MTHFR, COMT, VDR, BCMO1, FUT2, CBS. Returns personalized suggestions.

Inputs: no named parameters.

trait_reportTrait ReportA
Read-onlyIdempotent

Discover your genetic traits: caffeine metabolism, lactose tolerance, muscle type, bitter taste, sleep chronotype, alcohol flush, sun sensitivity, and more.

Inputs: no named parameters.

upload_genomeUpload GenomeA
Idempotent

Parse raw DNA data. Supports: 23andMe (.txt), Genera (.csv). CRITICAL: Genome files are 15-25MB (600k+ lines). NEVER read, inline, or chunk genome file contents — this WILL exceed your context window and crash the session. For REMOTE server (mcp.openpgx.ai): use get_upload_command to get a curl/python command, execute it in your code execution sandbox (or tell the user to run it in their terminal), then call load_profile with the returned code. For LOCAL server (stdio/npx): use file_path only. The url parameter is for direct download links (Google Drive, Dropbox, S3).

Inputs: file_path, url.

upload_genome_chunkSend Genome Data ChunkA
Idempotent

FOR PROGRAMMATIC MCP CLIENTS ONLY (not for LLM agents). LLM agents: use get_upload_command + load_profile instead. Sends a chunk of genome data for a chunked upload session.

Inputs: upload_id (required), chunk_index (required), data (required), is_last.

upload_genome_startStart Chunked Genome UploadA

FOR PROGRAMMATIC MCP CLIENTS ONLY (not for LLM agents). LLM agents: use get_upload_command + load_profile instead. This starts a chunked upload session for clients that can stream file contents without loading into context.

Inputs: filename.

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections.

Loading tool schema changelog…

    TDQS

    A3.6/5.0

    Scored across 16 tools

    Disambiguation3/5

    Several tools have overlapping purposes, notably the multiple 'full' report tools (diagnostic_all, full_pgx_report, full_report, full_risk_report) and multiple upload-related tools (upload_genome, upload_genome_chunk, upload_genome_start, get_upload_command, load_profile). Descriptions help but an agent could select the wrong one.

    Naming Consistency2/5

    Naming patterns are inconsistent: some use 'verb_noun' (check_medication, upload_genome), others use 'adjective_noun' (full_report, trait_report), and some are compound (get_upload_command, upload_genome_start). No coherent pattern across the set.

    Tool Count4/5

    16 tools cover a complex domain (pharmacogenomics, disease risk, traits, upload workflows) without being excessive. Each tool has a distinct role, though a few could potentially be consolidated.

    Completeness4/5

    The tool set covers major workflows: genome upload, medication and risk checking, comprehensive reports, and trait discovery. Minor gaps exist (e.g., no update/delete for saved research), but core needs are met.

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    • A
      license
      A
      quality
      B
      maintenance
      Upload your raw DNA file from 23andMe, AncestryDNA, MyHeritage, FamilyTreeDNA, or any VCF -- and watch a team of AI agents fan out across 16+ public genomics databases, share discoveries with each other in real time, and produce a comprehensive health report. Everything runs on your machine. Nothing is uploaded anywhere.
      24
      16
      MIT
    • A
      license
      C
      quality
      D
      maintenance
      Provides AI-powered access to major biological databases for GWAS and bioinformatics research. Enables natural language queries for protein, gene, variant, pathway, and drug discovery analysis.
      44
      1
      MIT
    • A
      license
      Not graded
      quality
      D
      maintenance
      Enables real-time pharmacogenomics analysis, including variant clinical significance, drug-gene interactions, and dosing guidelines, by connecting to ClinVar, PharmGKB, gnomAD, and other databases.
      1
      MIT
    • A
      license
      Not graded
      quality
      D
      maintenance
      Enables AI agents to query clinical genomics databases, retrieve supporting literature, analyze population genetics, and visualize biological pathways.
      18
      MIT