openpgx
OfficialClick on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@openpgx Can I take Ozempic?"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
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 ratiosPrivacy-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 openpgxCursor
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 openpgxRemote 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
npxinstall — 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-correctedDisease 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 checkTraits
"Trait report" → all 25+ traits
"Am I lactose intolerant?" → lactose persistence check
"Am I a morning person?" → chronotype analysisSupplements
"Supplement protocol" → MTHFR, COMT, VDR, BCMO1, FUT2, CBS
"Should I take methylfolate?" → based on your MTHFR statusWhat'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 |
| Parse raw DNA data (23andMe, Genera) |
| Smart drug lookup — brand names, generics, typos, categories |
| Complete pharmacogenomic report |
| Supplement optimization based on gene variants |
| Head-to-head drug comparison |
| Check genetic risk for a specific disease |
| Comprehensive disease risk report |
| All genetic traits analysis |
| 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
Find a pharmacogenomic study (PubMed, CPIC guidelines, PharmGKB)
Create a JSON file in
data/pgx/studies/following the naming pattern:{gene}_{year}_{slug}.jsonOpen 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 |
| yes | Specification version; const |
| no |
|
| no | Audit trail: |
| no | Profile only (no genotypes): |
| no | Raw measurements: each item has |
| no | Per-drug blocks: |
| no | Disease risk: |
| no |
|
| no | Hints for FHIR: |
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:
Brand > Generic — "Ozempic" > semaglutide (60+ brands including Venvanse, Rivotril, Marevan, Provigil, Stavigile)
Generic exact match — "semaglutide" > found
Fuzzy brand match — "ozmpic" > Ozempic > semaglutide
Fuzzy generic match — "sertralina" > sertraline
Category search — "weight loss", "antidepressant", "antidepressivo" > list of drugs
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 startProject 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.jsonPrivacy & Security
Your genetic data never leaves your computer
No cloud upload, no account, no tracking
Pre-commit hook prevents accidentally committing DNA files
.gitignoreblocks all genetic file patternsThe 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 toolscheck_medicationCheck MedicationARead-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 RiskARead-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 MedicationsARead-onlyIdempotent
Compare two medications head-to-head based on your genetic profile. Supports brand names.
Inputs: drug1 (required), drug2 (required).
diagnostic_allComplete Diagnostic ReportARead-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 ReportARead-onlyIdempotent
Generate a complete pharmacogenomic report with all your genes, phenotypes, and affected medication categories.
Inputs: no named parameters.
full_reportFull Genomic ReportARead-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 ReportARead-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 CommandARead-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 ProfileAIdempotent
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 OnlineARead-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 ProtocolARead-onlyIdempotent
Analyze genes related to supplement metabolism: MTHFR, COMT, VDR, BCMO1, FUT2, CBS. Returns personalized suggestions.
Inputs: no named parameters.
trait_reportTrait ReportARead-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 GenomeAIdempotent
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 ChunkAIdempotent
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
Scored across 16 tools
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 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.
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.
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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Medical RAG: semantic search for clinical guidelines, drug interactions, diagnoses & EHR data.
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