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K01 Synthetic Health Data

K01 Synthetic Health Data

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github.com/K01labsHealth1Updated 3w ago
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About K01 Synthetic Health Data

K01 MCP Server provides synthetic health data tools for AI agents and LLM-powered workflows. It generates and queries FHIR R4/R5 data with differential privacy; the server does not handle real patient records.

Key features

  1. 01Generate synthetic patient cohorts
  2. 02Search patients with healthcare filters
  3. 03Retrieve patient, medication, and condition records
  4. 04Compare patient data across FHIR R4 and R5
  5. 05Use deterministic seeds for reproducible data

Use cases

  1. 01Create synthetic cohorts for development and testing
  2. 02Query virtual patient records by clinical criteria
  3. 03Compare FHIR R4 and R5 representations

MCP support

The MCP server exposes tools to generate synthetic cohorts, search patients, retrieve records, and compare FHIR versions. Clients connect to the hosted server using a Bearer token.

Configuration

Remote servers need no local install. Pick your client, then copy the config or install in one click.

Endpointhttps://mcp.k01.is/mcp
Copy install command
Claude CodeTerminal
Cursor.cursor/mcp.json
VS Code.vscode/mcp.json
Claude Desktopclaude_desktop_config.json
Windsurf~/.codeium/windsurf/mcp_config.json

Run commands in a terminal; paste JSON into the client’s MCP config and restart. Servers that need sign-in will prompt on first connect.

All endpoints
Streamable HTTPhttps://mcp.k01.is/mcpAuth required

Tools

Details

From the project README

K01 MCP Server

License: MIT MCP

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

ToolDescriptionKey Parameters
generate_synthetic_cohortGenerate synthetic patient cohorts with demographic and clinical constraintscount, age_min/age_max, gender, condition (ICD-10), seed, locale
search_patientsSearch a virtual patient database with advanced healthcare filtersgender, birthdate, numberOfMedicinesFrom/To, conditionTypes, locale
get_patient_recordRetrieve a complete patient record with medication and condition summariespatient_id, fhir_version
get_patient_medicationsGet detailed medication records with ATC codes and therapeutic indicationspatient_id, seed, fhir_version
get_patient_conditionsRetrieve patient conditions with ICD-10 codes and localized namespatient_id, seed, fhir_version
compare_fhir_versionsCompare the same patient data across FHIR R4 and R5patient_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.

Comments

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An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.