MCP Server × PostgreSQL on MIMIC‑IV
About
# MCP Server × PostgreSQL on MIMIC‑IV > **Project goal:** Evaluate whether a Model Context Protocol (MCP) Server layered on Azure Database for PostgreSQL can match—or outperform—direct SQL while reducing developer effort for large‑scale clinical analytics. --- ## 1. Problem Statement Clinical datasets such as MIMIC‑IV…
Details
- License
- MIT license
Explore
- MCP‑based abstraction over Azure Database for PostgreSQL.
- Designed for clinical dataset MIMIC‑IV (millions of rows, hundreds of attributes).
- Supports both password and Microsoft Entra authentication.
- Includes benchmark scripts to compare performance with direct SQL.
- Example benchmarks show up to 33% faster query execution for COUNT(*) on EMAR data.
- Reduces lines of code (LOC) by 66% compared to raw SQL for a sample workload.
Setting up with Highlight
This MCP is not yet compatible with Highlight’s one-click setup. However, you can still use it with Highlight by following these steps:
- Download and install Highlight from highlightai.com/download
- Navigate to the plugins tab and select "Add Custom Plugin"
-
Configure the plugin with the settings below
Plugin Name
MCP Server × PostgreSQL on MIMIC‑IVCommand (node, npx, python, etc.)Please refer to the README for specific instructions on how to obtain API keys or other required environment variables.
- Enable "Start Automatically" if you want the plugin to start when Highlight launches
From the repository
- Python 3.10+
- Node 18+ (optional, for additional MCP tooling)
- Azure Database for PostgreSQL – Flexible Server (Standard_D4s v5 or larger)
- MIMIC‑IV credential & data download permission
```bash
Claude Desktop / Cursor
Paste into your MCP client config file to install this server.
{
"mcpServers": {
"mcp server \u00d7 postgresql on mimic\u2011iv": {
"mimic-mcp-server": {
"command": "python",
"args": [
"02_bulk_copy.py",
"--csv-root",
"/path/to/mimic-iv"
]
}
}
}
}McpServers
{
"mimic-mcp-server": {
"command": "python",
"args": [
"02_bulk_copy.py",
"--csv-root",
"/path/to/mimic-iv"
]
}
}> Project goal: Evaluate whether a Model Context Protocol (MCP) Server layered on Azure Database for PostgreSQL can match—or outperform—direct SQL while reducing developer effort for large‑scale clinical analytics.
---
1. Problem Statement
Clinical datasets such as MIMIC‑IV contain tens of millions of rows and hundreds of attributes. Writing and maintaining raw SQL against such breadth is error‑prone and slow. We ask:
_Can an MCP‑based abstraction simultaneously improve developer productivity and sustain (or enhance) runtime efficiency compared with traditional SQL‑only workflows?_
To answer this we compare direct SQL against an MCP Server → Postgres path on identical workloads—ranging from a simple COUNT(*) on mimiciv_hosp.emar_detail (≈ 87 M rows) to multi‑table note aggregations.
---
2. Repository Layout
.
├── mimic-postgres/ # Scripts to create schemas & ingest MIMIC‑IV CSVs
├── src/ # Fork of azure_postgresql_mcp with env configs
└── README.md # This file
---
3. Prerequisites
- Python 3.10+
- Node 18+ (optional, for additional MCP tooling)
- Azure Database for PostgreSQL – Flexible Server (Standard_D4s v5 or larger)
- MIMIC‑IV credential & data download permission
```bash
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