How do I deploy pg-mcp server?
You can deploy pg-mcp by cloning its repository from GitHub and following the setup instructions provided in the documentation site at https://stuzero.github.io/pg-mcp/.
What databases does pg-mcp support?
pg-mcp is specifically designed to work with PostgreSQL databases, exposing their data as structured context to LLMs.
Is pg-mcp secure for production use?
Yes, pg-mcp supports scoped and secure access to PostgreSQL data, ensuring safe interaction between LLMs and your database.
Can pg-mcp handle real-time data updates?
Yes, pg-mcp can expose live PostgreSQL data, enabling real-time querying and interaction by AI models.
Does pg-mcp support integration with multiple LLM providers?
Yes, pg-mcp is provider-agnostic and works with models like OpenAI, Claude, and Gemini.
How does pg-mcp improve AI workflows?
By providing structured, real-time database context, pg-mcp enables AI models to perform multi-step reasoning and data-driven decision making.
Where can I find the source code for pg-mcp server and client?
The pg-mcp server source code is at https://github.com/stuzero/pg-mcp-server and the client at https://github.com/stuzero/pg-mcp-client.
Can I customize pg-mcp for my specific PostgreSQL schema?
Yes, pg-mcp is designed to be flexible and can be configured to expose custom schemas and tables as needed.