About K01 Synthetic Health Data
K01 Synthetic Health Data is an MCP server in the Healthcare category: privacy-preserving synthetic health data generation. FHIR R4/R5 compliant.
Install
git clone https://github.com/K01labs/k01-mcp-serverThis server has no ConduID identity, so agent calls to it are not receipted. Pin the version you install and review the source before granting it credentials.
Tools (6)
compare_fhir_versionsCompare the same patient data across FHIR R4 and R5
Parameters not published.
generate_synthetic_cohortGenerate synthetic patient cohorts with demographic and clinical constraints
Parameters not published.
get_patient_conditionsRetrieve patient conditions with ICD-10 codes and localized names
Parameters not published.
get_patient_medicationsGet detailed medication records with ATC codes and therapeutic indications
Parameters not published.
get_patient_recordRetrieve a complete patient record with medication and condition summaries
Parameters not published.
search_patientsSearch a virtual patient database with advanced healthcare filters
Parameters not published.
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README
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.
README copy captured when this server was indexed; it may lag the repository. The original is authoritative.