omop_mcp
Verified Safeby OHNLP
Overview
Maps clinical terminology to OMOP (Observational Medical Outcomes Partnership) concepts using Large Language Models (LLMs) via the Model Context Protocol (MCP) server.
Installation
uv --directory <path-to-local-repo> run omop_mcpEnvironment Variables
- AZURE_OPENAI_ENDPOINT
- AZURE_OPENAI_API_KEY
- AZURE_API_VERSION
- MODEL_NAME
- OPENAI_API_KEY
Security Notes
The server makes external HTTP requests to known OMOP resources (Athena OHDSI) which is appropriate. API keys are managed through environment variables via `dotenv`. The `batch_map_concepts_from_csv` tool takes a `csv_path` as an argument; if the server were exposed publicly to untrusted users without input validation, this could potentially be leveraged for arbitrary local file access (e.g., path traversal). However, the intended use case (e.g., Claude Desktop integration) implies a local or controlled environment where this risk is mitigated.
Similar Servers
gis-mcp
A Model Context Protocol (MCP) server that provides AI agents and LLMs with comprehensive GIS capabilities, enabling geospatial analysis, data gathering, and transformations through natural language.
pyomop
A Python library providing tools for managing OMOP Common Data Model databases, including LLM-powered natural language querying, FHIR-to-OMOP data conversion, and PyHealth/PLP compatibility for machine learning pipelines.
mcp-advisor
Provides LLMs and humans with structured access to the Model Context Protocol (MCP) specification and documentation for understanding and compliance evaluation.
omcp
The server enables Large Language Models (LLMs) to securely query and analyze healthcare data stored in the OMOP Common Data Model format through a standardized Model Context Protocol interface.