DBeast
About
Expert-level PostgreSQL database analysis MCP server for AI assistants.
Details
- Author
- snss10
- Categories
- Database, Other
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Setup
Install DBeast in your MCP client (Claude Desktop, Cursor, Windsurf, and others).
Repository: https://github.com/snss10/DBeast
Follow the installation instructions in the repository README, then restart your MCP client.
A PostgreSQL MCP server that gives AI assistants expert DBA capabilities.
Quick Start·Demo·Tools·Safety·Configuration·Docs
DBeast connects AI assistants such as Claude, Cursor, Windsurf, and VS Code Copilot to PostgreSQL through theModel Context Protocol. Instead of exposing one broadexecute_sqlescape hatch, DBeast provides21 focused toolsfor schema discovery, safe query execution, impact analysis, performance review, security checks, maintenance reporting, replication monitoring, and data quality inspection.
Watch Claude use DBeast MCP tools to audit a PostgreSQL database, identify security and maintenance risks, and preview cleanup impact without executing destructive SQL.
AI assistant --MCP stdio--> DBeast server --asyncpg--> PostgreSQL Claude/Cursor Python local Local, RDS, Windsurf/VS Code subprocess Supabase, Neon
DBeast runs as a local stdio MCP server. Your IDE or desktop assistant starts it as a subprocess and passes database credentials through environment variables. The assistant calls DBeast tools, DBeast queries PostgreSQL, and structured results come back to the assistant. No HTTP service or extra infrastructure is required.
git clone https://github.com/snss10/DBeast.git cd DBeast pip install -e .
Optional: copy.env.exampleto.envand set your database credentials.
{ "mcpServers": { "dbeast": { "type": "stdio", "command": "python", "args": ["/absolute/path/to/DBeast/src/server.py"], "env": { "DATABASE_URL": "postgresql://user:password@localhost:5432/mydb" } } } }
Once connected, your assistant can answer quick lookup questions and also run multi-step database investigations.
Show me the schema for the orders table. Which queries are slowest right now? Run a security audit on the public schema. Generate a Mermaid ERD for the sales schema.
Before I archive old sessions, estimate how many rows would be affected, identify related tables, and tell me the rollback risk. Investigate why the dashboard query is slow, explain the execution plan, and suggest safe indexes. Review the public schema for maintenance issues, security risks, and data quality problems, then summarize the top priorities. Compare table growth, dead tuples, and index health across all schemas and recommend what to vacuum or reindex first.
DBeast exposes 21 MCP tools across 10 categories.
get_schema() get_schema(schema='public')
execute_query(query='SELECT * FROM orders ORDER BY created_at DESC')
analyze_impact(query='DELETE FROM sessions WHERE last_active < now() - interval ''30 days''')
database_health() maintenance_analysis(schema='public') query_performance()
Most analysis tools accept aschemaparameter:
maintenance_analysis(schema='public') -> analyze one schema maintenance_analysis(schema='all') -> analyze every schema get_schema(format='mermaid') -> generate an ERD diagram
# Full URL DATABASE_URL=postgresql://user:pass@host:5432/db # Or individual variables DB_HOST=localhost DB_PORT=5432 DB_USER=postgres DB_PASSWORD=secret DB_NAME=mydb DB_SSLMODE=prefer # Or AWS Secrets Manager AWS_SECRET_NAME=my-rds-secret AWS_REGION=us-west-2
connect(url='postgresql://user:pass@host:5432/db') connect(host='localhost', user='postgres', password='secret', database='mydb') connect(aws_secret_name='my-secret', aws_region='us-west-2')
SeeSETUP.mdfor the complete configuration reference.
Formatted and JSON responses use a consistent wrapper:
{ "success": true, "data": { "...": "..." }, "meta": { "connected": true, "source": "tool" } }
DBeast logs MCP tool calls for accountability and debugging.
DBEAST_AUDIT_ENABLED=true DBEAST_AUDIT_DIR=logs/mcp_audit
Audit files are stored as daily markdown files and include timestamps, tool names, durations, masked parameters, truncated responses, and errors.
pip install -e ".[dev]" pre-commit install pytest tests/ -v ruff check src/ tests/ ruff format src/ tests/
Start the optional local PostgreSQL test database:
- SETUP.md- Full client setup, connection scenarios, configuration, and troubleshooting
- CONTRIBUTING.md- Development setup, tests, style, commits, and PR process
- CODE_OF_CONDUCT.md- Community guidelines
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