Summary
Connects to K01's hosted synthetic health data API to generate FHIR R4/R5 compliant patient records without touching real PHI. Exposes six tools: generate cohorts with demographic and clinical constraints (age, gender, ICD-10 conditions), search a virtual patient database, retrieve complete records or specific medication/condition lists, and compare the same data across FHIR versions. Uses differential privacy and deterministic seeding for reproducible test data. Reach for this when you're building healthcare AI workflows and need realistic EHR data for development, testing prompt chains against patient records, or validating FHIR implementations without compliance overhead. Requires an API key from K01.
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K01 MCP Server
Generate and query clinically realistic synthetic health data directly from your AI workflow.
K01's MCP server gives AI agents and LLM-powered tools access to privacy-preserving synthetic health data generation. Generate FHIR-compliant patient cohorts, query structured EHR data, and compare across FHIR versions — all with built-in differential privacy. No real patient data is involved at any point.
This repository contains documentation and configuration for the K01 MCP server. The server implementation is proprietary and runs on K01 infrastructure.
Available Tools
| Tool | Description | Key Parameters |
|---|---|---|
generate_synthetic_cohort | Generate synthetic patient cohorts with demographic and clinical constraints | count, age_min/age_max, gender, condition (ICD-10), seed, locale |
search_patients | Search a virtual patient database with advanced healthcare filters | gender, birthdate, numberOfMedicinesFrom/To, conditionTypes, locale |
get_patient_record | Retrieve a complete patient record with medication and condition summaries | patient_id, fhir_version |
get_patient_medications | Get detailed medication records with ATC codes and therapeutic indications | patient_id, seed, fhir_version |
get_patient_conditions | Retrieve patient conditions with ICD-10 codes and localized names | patient_id, seed, fhir_version |
compare_fhir_versions | Compare the same patient data across FHIR R4 and R5 | patient_id, resource_type |
All tools support both FHIR R4 and R5. See docs/tools.md for full parameter documentation.
Quick Start
Claude Desktop
Add to your Claude Desktop configuration (Settings > MCP Servers):
{
"mcpServers": {
"k01": {
"url": "https://mcp.k01.is/mcp",
"headers": {
"Authorization": "Bearer YOUR_API_KEY"
}
}
}
}
Cursor
Add to your Cursor MCP configuration (.cursor/mcp.json):
{
"mcpServers": {
"k01": {
"url": "https://mcp.k01.is/mcp",
"headers": {
"Authorization": "Bearer YOUR_API_KEY"
}
}
}
}
Restart the application after adding the configuration. The K01 tools will appear in the tool list.
Authentication
The K01 MCP server uses Bearer token authentication. Include your API key in the Authorization header.
To get an API key, contact K01 at k01.is. Self-service key provisioning is coming soon.
See docs/authentication.md for details.
Examples
Generate a diabetic cohort
"Generate 100 patients aged 40-65 with Type 2 diabetes"
The generate_synthetic_cohort tool creates a FHIR Bundle with Patient, Condition, MedicationStatement, Procedure, and Observation resources:
generate_synthetic_cohort(count=100, age_min=40, age_max=65, condition="E11.9", seed=42)
Search for complex patients
"Find patients with 3+ medications and chronic conditions"
search_patients(numberOfMedicinesFrom=3, conditionTypes="chronic", _count=20)
Compare FHIR versions
"How do this patient's medications look in R4 vs R5?"
compare_fhir_versions(patient_id="0101302989", resource_type="Medication")
See docs/examples.md for more workflows.
Privacy and Security
All data generated by K01 is fully synthetic. No real patient data is used, stored, or referenced at any point in the pipeline.
- Differential privacy — noise injection with configurable epsilon values ensures generated distributions cannot be reverse-engineered to identify real individuals
- No training on real data — the generation models are built from published clinical statistics and pharmacological references, not patient records
- FHIR-compliant output — all resources conform to HL7 FHIR R4/R5 specifications, making them safe drop-in replacements for real data in development and testing
- Deterministic seeding — use seeds for reproducible results without persisting any patient data server-side
Links
License
This repository (documentation, configuration, and examples) is licensed under MIT. The K01 server implementation is proprietary.
Featured
Keep your Mac awake while Claude Code and 40+ AI agents run. Sleeps when they're idle.
One time payment $9 →Integrate web data into your AI product. One API to scrape website & brand data.
Get API Key Now →Agent, run crypto. Access onchain data & trade routes via 1inch.
Install now →On Capafy, your Skill runs online 24/7 as an agent product, and you get paid every time someone uses it.
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