K01 Synthetic Health Data
Privacy-preserving synthetic health data generation. FHIR R4/R5 compliant.
Install
Add this to your client's MCP configuration (Claude Desktop: claude_desktop_config.json; Cursor: .cursor/mcp.json; VS Code and others use the same "mcpServers" shape).
claude mcp add --transport http k01-synthetic-health-data 'https://mcp.k01.is/mcp' --header 'Authorization: <API key for authentication (Bearer token)>'{
"mcpServers": {
"k01-synthetic-health-data": {
"type": "http",
"url": "https://mcp.k01.is/mcp",
"headers": {
"Authorization": "<API key for authentication (Bearer token)>"
}
}
}
}Documentation
From the project's README on GitHub, refreshed weekly. Links open on GitHub.
Contents
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. The server never handles real patient records: it serves synthetic data from generators that were trained on real records, under differential privacy, inside a separate controlled environment.
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 this server returns is fully synthetic. No real patient data passes through the server, is stored by it or is referenced by it.
- Differential privacy — noise injection with configurable epsilon values ensures generated distributions cannot be reverse-engineered to identify real individuals
- Real data stays behind the firewall — the generators are trained on real records under differential privacy inside a separate controlled environment; only the trained generators, never the records, are reachable from this server
- 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.
Frequently asked questions
What is K01 Synthetic Health Data?
Privacy-preserving synthetic health data generation. FHIR R4/R5 compliant.
How do I install K01 Synthetic Health Data?
It runs as a remote server. In Claude Code: claude mcp add --transport http k01-synthetic-health-data 'https://mcp.k01.is/mcp' --header 'Authorization: <API key for authentication (Bearer token)>'. For other clients, paste the JSON configuration from this page into their MCP settings.
Does K01 Synthetic Health Data need an API key?
Yes. It expects Authorization, which you set in its configuration.
Is K01 Synthetic Health Data open source?
Yes. Its code is on GitHub under the MIT license.
Who maintains K01 Synthetic Health Data?
It is published by K01labs. AI.tn lists it but does not maintain or endorse it.
From the official MCP Registry. Listing is not an endorsement: review a server's code and permissions before giving it access to your data.