Timescale is a company that provides a time-series database built on PostgreSQL. They offer TimescaleDB, which is designed for storing and analyzing time-series data at scale while maintaining SQL compatibility.
A production-grade Model Context Protocol server for PostgreSQL. Lets AI agents safely inspect, query, operate, and tune a Postgres database — over 100 tools spanning catalog introspection, query intelligence, natural-language SQL, structural diffs, hybrid search, graph queries, data movement, live ops, and more.
186
10
MIT
Why this server?
Detects TimescaleDB extensions and flags its capabilities within PostgreSQL.
MCP server providing deep PostgreSQL context to AI assistants, including schema DDL, index health, foreign key associations, query execution plans, and performance statistics via read-only tools.
12
1
MIT
Why this server?
Allows interacting with TimescaleDB (Postgres-compatible) databases for querying, schema management, and transactions.
Enables AI assistants to interact with multiple SQL databases (PostgreSQL, MySQL, SQL Server, SQLite, Trino) through MCP, reusing existing DBeaver connections for queries, schema management, and transactions, with SSH tunneling and read-only mode support.
22
126 npm
MIT
Why this server?
Provides support for TimescaleDB, a time-series relational database, enabling query execution and schema management.
A local MCP server that fetches official library documentation (llms.txt-first), caches it to disk, and serves relevant sections to coding agents offline with deterministic retrieval.
3
3 npm
MIT
Why this server?
Adds support for TimescaleDB, a time-series database, to query and analyze time-stamped IoT data via natural language.
MCP server for chatting with physical-world data from robotics, drones, automotive, and IoT sources using natural language. It generates auditable SQL queries over Apache Arrow/DuckDB to let you analyze, summarize, and build data pipelines.
25
397
Apache 2.0
Why this server?
Provides documentation and AI-optimized skills for TimescaleDB, including best practices for time-series data management and performance.
Enables AI assistants to manage and query Tiger Cloud database services, including service lifecycle operations, database connections, and SQL execution.
