Which integrations are available for this server?

Allows retrieving database credentials from AWS Secrets Manager for secure connection setup. Supports connecting to PostgreSQL databases hosted on Fly.io. Provides tools to connect to and manage PostgreSQL databases, including schema discovery, query analysis, health checks, security audits, and more. Supports connecting to PostgreSQL databases hosted on Railway. Supports connecting to PostgreSQL databases hosted on Render. Supports connecting to Supabase PostgreSQL databases using pooler connection strings.

DBeast by snss10

A PostgreSQL MCP server that gives AI assistants expert DBA capabilities.

Python 3.11+ PostgreSQL 12+ MCP Compatible License: MIT

Quick Start · Demo · Tools · Safety · Configuration · Docs

DBeast connects AI assistants such as Claude, Cursor, Windsurf, and VS Code Copilot to PostgreSQL through the Model Context Protocol. Instead of exposing one broad execute_sql escape hatch, DBeast provides 21 focused tools for schema discovery, safe query execution, impact analysis, performance review, security checks, maintenance reporting, replication monitoring, and data quality inspection.

Demo

Watch Claude use DBeast MCP tools to audit a PostgreSQL database, identify security and maintenance risks, and preview cleanup impact without executing destructive SQL.

DBeast MCP demo: Claude audits a PostgreSQL database

Watch the demo on YouTube

Related MCP server: Postgres MCP Pro

How It Works

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.

Quick Start

1. Install

git clone https://github.com/snss10/DBeast.git
cd DBeast
pip install -e .

For development:

pip install -e ".[dev]"

Optional: copy .env.example to .env and set your database credentials.

2. Verify

dbeast

Or run the source entry point directly:

python src/server.py

3. Configure Your MCP Client

Minimal Cursor or Windsurf config:

{
  "mcpServers": {
    "dbeast": {
      "type": "stdio",
      "command": "python",
      "args": ["/absolute/path/to/DBeast/src/server.py"],
      "env": {
        "DATABASE_URL": "postgresql://user:password@localhost:5432/mydb"
      }
    }
  }
}

Common config locations:

Client	Config location
Cursor	`.mcp.json` in project root, or `~/.cursor/.mcp.json` globally
VS Code	`.vscode/settings.json` or user settings with key `mcp.servers`
Claude Desktop on macOS	`~/Library/Application Support/Claude/claude_desktop_config.json`
Claude Desktop on Windows	`%APPDATA%\Claude\claude_desktop_config.json`
Windsurf	`.mcp.json`

See SETUP.md for full client examples, Docker, RDS, Supabase, Neon, SSH tunnels, AWS Secrets Manager, and troubleshooting.

4. Ask Simple or Complex Questions

Once connected, your assistant can answer quick lookup questions and also run multi-step database investigations.

Simple examples:

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.

More complex examples:

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.

Tools

DBeast exposes 21 MCP tools across 10 categories.

Connection

Tool	Description
`connect`	Connect to PostgreSQL, check current status, or discover local databases
`disconnect`	Close the current database connection
`health_check`	Verify connectivity, pool health, PostgreSQL version, and extensions

Schema Discovery

Tool	Description
`get_schema`	List schemas, tables, columns, indexes, relationships, and optional Mermaid ERDs
`dependency_analysis`	Map object dependencies before renaming, dropping, or changing database objects

Data Access

Tool	Description
`execute_query`	Run read-only `SELECT` queries with automatic row-limit injection

Query Analysis

Tool	Description
`analyze_query`	Parse and inspect query structure, warnings, and optimization hints
`query_optimizer`	Recommend indexes and rewrites for a given query
`analyze_impact`	Preview write-query impact, risk level, affected rows, and rollback context without executing

Database Health

Tool	Description
`database_health`	Review cache hit rates, connections, transaction age, table health, and overall health signals
`query_performance`	Report slow or expensive queries from PostgreSQL statistics

Security

Tool	Description
`security_audit`	Inspect roles, privileges, superuser accounts, and public schema exposure
`sensitive_data_scan`	Detect likely PII or secrets by column names and schema patterns

Maintenance

Tool	Description
`maintenance_analysis`	Review vacuum status, dead tuples, analyze timestamps, and index health
`partition_analysis`	Inspect partition health, row distribution, and missing partition risks

Data Quality

Tool	Description
`data_quality_report`	Analyze null rates, cardinality, value distributions, and outliers
`duplicate_detection`	Find duplicate rows across selected key columns

Server Config

Tool	Description
`configuration_review`	Review PostgreSQL configuration and tuning opportunities
`replication_status`	Inspect replication lag, WAL sender/receiver state, and replication slots

Audit

Tool	Description
`get_audit_logs`	Retrieve logged MCP tool calls for a given date
`list_audit_files`	List available audit log files

Recommended Workflow

Start by discovering schemas:

get_schema()
get_schema(schema='public')

Run safe read queries:

execute_query(query='SELECT * FROM orders ORDER BY created_at DESC')

Preview risky writes:

analyze_impact(query='DELETE FROM sessions WHERE last_active < now() - interval ''30 days''')

Check health and maintenance:

database_health()
maintenance_analysis(schema='public')
query_performance()

Most analysis tools accept a schema parameter:

maintenance_analysis(schema='public')  -> analyze one schema
maintenance_analysis(schema='all')     -> analyze every schema
get_schema(format='mermaid')           -> generate an ERD diagram

Supported Databases

Provider	Connection method
Local PostgreSQL	`DATABASE_URL` or individual `DB_*` variables
Docker PostgreSQL	Explicit variables or `connect(discover=true)`
AWS RDS / Aurora	Direct URL, SSH tunnel, or AWS Secrets Manager
Supabase	Pooler connection string from Dashboard settings
Neon	Connection string from Console connection details
Railway / Render / Fly.io	Provider connection string
Any PostgreSQL host	Standard PostgreSQL URL

Configuration

Choose one connection method.

# 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

You can also connect at runtime:

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')

Key settings:

Variable	Default	Description
`DBEAST_DEFAULT_ROW_LIMIT`	`100`	Max rows returned by `execute_query`
`DBEAST_QUERY_TIMEOUT`	`300`	Query execution timeout in seconds
`DBEAST_COMMAND_TIMEOUT`	`300`	SQL command timeout in seconds
`DBEAST_SSL_VERIFY`	`true`	Set `false` for SSH tunnels where certificates do not match `localhost`
`DBEAST_SCHEMA_CACHE_TTL`	`60`	Schema cache TTL in seconds, `0` disables caching
`DBEAST_AUDIT_ENABLED`	`true`	Log MCP tool calls
`DBEAST_AUDIT_DIR`	`logs/mcp_audit`	Audit log directory

See SETUP.md for the complete configuration reference.

Safety Model

Query type	What DBeast does
`SELECT`	Executes with automatic row limits
`INSERT` / `UPDATE` / `DELETE`	Never executed; returns an impact preview
`DROP` / `TRUNCATE`	Never executed; reports affected objects and risk

Formatted and JSON responses use a consistent wrapper:

{
  "success": true,
  "data": { "...": "..." },
  "meta": {
    "connected": true,
    "source": "tool"
  }
}

Audit Logging

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.

Development

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:

docker compose up -d postgres

Legacy Compose:

docker-compose up -d postgres

Documentation

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

License

MIT

DBeast

Demo

How It Works

Quick Start

1. Install

2. Verify

3. Configure Your MCP Client

4. Ask Simple or Complex Questions

Tools

Connection

Schema Discovery

Data Access

Query Analysis

Database Health

Security

Maintenance

Data Quality

Server Config

Audit

Recommended Workflow

Supported Databases

Configuration

Safety Model

Audit Logging

Development

Documentation

License

Maintenance

Resources

Tools

Latest Blog Posts

MCP directory API