PostgreSQL
This PostgreSQL MCP server implementation by Logesh provides AI assistants with direct database query execution capabilities for PostgreSQL databases. Built with Python using psycopg2, loguru, and FastMCP, it offers a single query_data tool that handles both SELECT queries (returning fetched rows with metadata) and write operations (INSERT, UPDATE, DELETE) with automatic transaction management. The implementation supports both stdio and HTTP transport modes, includes comprehensive error handling and logging, and is preconfigured to connect to a 'litellm' database with standard PostgreSQL credentials, making it suitable for AI-assisted database administration, data analysis workflows, and educational purposes where conversational database interaction is needed.
Composite of vulnerability cleanliness, spec conformance, provenance, stability, and usage signals — scanned and weighted by Cognium. Human and agent signals are tracked separately. Last scanned 2026-09-19.
Scan details: Circle-IR · 2026-09-19 · Appeal
View full trust & usage report →Metadata
- Version
- 1.0.0
- Skill type
- atomic
- Execution layer
- mcp-remote
- Category
- database
- Source
- PulseMCP
- Repository
- github.com/logesh-001/postgresmcp
- Author type
- human
- Last scanned
- 2026-09-19
- Updated
- 2026-09-19
Use via MCP
Resolve PostgreSQL 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.