A MCP server allowing LLM agents to easily connect and retrieve data from any database
{
"mcpServers": {
"turbular": {
"command": "<see-readme>",
"args": []
}
}
}No install config available. Check the server's README for setup instructions.
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A MCP server allowing LLM agents to easily connect and retrieve data from any database
Is it safe?
No package registry to scan.
No authentication — any process on your machine can connect.
License not specified.
Is it maintained?
Last commit 250 days ago. 99 stars.
Will it work with my client?
Transport: stdio. Works with Claude Desktop, Cursor, Claude Code, and most MCP clients.
No automated test available for this server. Check the GitHub README for setup instructions.
No known vulnerabilities.
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Turbular is an open-source Model Context Protocol (MCP) server that enables seamless database connectivity for Language Models (LLMs). It provides a unified API interface to interact with various database types, making it perfect for AI applications that need to work with multiple data sources.
| Database Type | Status | Icon |
|---------------|--------|----------------------------------------------------------------------------------------------------------------------------------------------------|
| PostgreSQL | ✅ | |
| MySQL | ✅ |
|
| SQLite | ✅ |
|
| BigQuery | ✅ |
|
| Oracle | ✅ |
|
| MS SQL | ✅ |
|
| Redshift | ✅ |
|
Clone the repository:
git clone https://github.com/raeudigerRaeffi/turbular.git
cd turbular
Start the development environment:
docker-compose -f docker-compose.dev.yml up --build
Test the connection:
./scripts/test_connection.py
Install Python 3.11 or higher
Install dependencies:
pip install -r requirements.txt
Run the server:
uvicorn app.main:app --reload
POST /get_schema
Retrieve the schema of a connected database for your LLM agent.
Parameters:
db_info: Database connection argumentsreturn_normalize_schema (optional): Return schema in LLM-friendly formatPOST /execute_query
Optimizes query and then e