PostgreSQL Alchemy
This MCP implementation, developed by rhabraken, provides a standardized interface for AI models to interact with PostgreSQL databases. Built using Python and leveraging SQLAlchemy, it offers a flexible solution for database operations across various SQL dialects. The implementation focuses on simplifying database access through environment variable configuration and Docker containerization, making it easy to set up and use in different environments. By connecting AI models with relational databases, this server enables sophisticated querying and data manipulation scenarios, enhancing the ability to retrieve and analyze structured data. It's particularly useful for applications and AI assistants that require programmatic access to SQL databases, facilitating use cases such as data analysis, reporting, and automated database management tasks.
Composite of vulnerability cleanliness, spec conformance, provenance, stability, and usage signals — scanned and weighted by Cognium. Human and agent signals are tracked separately.
View full trust & usage report →Metadata
- Version
- 1.0.0
- Skill type
- atomic
- Execution layer
- mcp-remote
- Category
- database
- Source
- PulseMCP
- Repository
- github.com/rhabraken/mcp-python
- Author type
- human
- Updated
- 2026-04-25
Use via MCP
Resolve PostgreSQL Alchemy from your agent
Streamable HTTP transport at https://api.skillsregistry.net/mcp. No auth for read tools. Discovery: .well-known/mcp.json.
One command in your shell — Claude Code wires it up and verifies the connection. Run /mcp in any session to confirm.
claude mcp add --transport http --scope user skillsregistry https://api.skillsregistry.net/mcp--scope user for --scope project to commit it to .mcp.json.