TimescaleDB

About

This Python-based Model Context Protocol (MCP) server provides a robust bridge for AI assistants to seamlessly interact with TimescaleDB. It offers high-performance asynchronous database operations, efficient connection pooling, and comprehensive schema introspection via MCP resources. With 6 specialized MCP tools for querying and describing tables/hypertables, along with pre-built prompts for common time-series analysis tasks, it empowers AI to explore, analyze, and manage time-series data securely with parameterized queries, comprehensive error handling, and structured logging.

Key Features

• SQL Injection Prevention using parameterized queries throughout
• Pre-built MCP Prompts for time-series analysis and schema exploration
• Async Database Operations with `asyncpg` for high performance
• 0 GitHub stars
• 6 MCP Tools: Execute queries, list/describe tables & hypertables, query time-series
• MCP Resources for schema introspection (tables, hypertables)

Use Cases

• Allowing AI assistants to query and analyze time-series data in TimescaleDB
• Enabling AI assistants to explore TimescaleDB schema and hypertable structures
• Integrating AI-powered tools with existing TimescaleDB instances for advanced data interaction