合成健康数据生成与查询MCP服务

K01 MCP Server 是一款面向AI代理和大语言模型工作流的合成健康数据生成与查询服务,可生成符合FHIR标准并具备差分隐私保护的模拟患者数据,支持患者队列生成、EHR查询和FHIR版本对比,适用于医疗开发、测试和AI集成场景。

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在 GitHub 查看 K01labs/k01-mcp-server

使用说明

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.