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pg-aiguide

timescale · timescale/pg-aiguide

This MCP server and Claude plugin provides AI-optimized PostgreSQL expertise, enabling agents to generate dramatically better, modern, and best-practice PostgreSQL code.

MCP server and Claude plugin for Postgres skills and documentation. Helps AI coding tools generate better PostgreSQL code.

1.9k stars113License: Apache-2.0Updated 17 days ago0 opens

Preview

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What you can do with it

  • Generate robust PostgreSQL schemas with appropriate constraints and indexes for an e-commerce website.
  • Get AI-generated PostgreSQL code that uses modern features and version-aware best practices.
  • Find semantic information within the official PostgreSQL manual directly from the AI agent.

Safety check

Safe

No risky patterns found in the files we scanned.

Automated scan of the repo files. Always review code you run.

From the README

pg-aiguide

AI-optimized PostgreSQL expertise for coding assistants

pg-aiguide helps AI coding tools write dramatically better PostgreSQL code. It provides:

  • Semantic search across the official PostgreSQL manual (version-aware)
  • AI-optimized “skills” — curated, opinionated Postgres best practices used automatically by AI agents
  • Extension ecosystem docs, starting with TimescaleDB, with more coming soon

Use it as:

  • Agent Skills via npx skills — works with Claude Code, Cursor, Codex, Gemini CLI, and 40+ other agents
  • a public MCP server that can be used with any AI coding agent, or
  • a Claude Code plugin optimized for use with Claude's native skill support.

⭐ Why pg-aiguide?

AI coding tools often generate Postgres code that is:

  • outdated
  • missing constraints and indexes
  • unaware of modern PG features
  • inconsistent with real-world best practices

pg-aiguide fixes that by giving AI agents deep, versioned PostgreSQL knowledge and proven patterns.

See the difference

https://github.com/user-attachments/assets/5a426381-09b5-4635-9050-f55422253a3d

Video Transcript

Prompt given to Claude Code:

Please describe the schema you would create for an e-commerce website two times, first with the tiger mcp server disabled, then with the tiger mcp server enabled. For each time, write the schema to its own file in the current working directory. Then compare the two files and let me know which approach generated the better schema, using both qualitative and quantitative reasons. For this example, only use standard Postgres.

Result (summarized):

  • 4× more constraints
  • 55% more indexes (including partial/expression indexes)
  • PG17-recommended patterns
  • Modern features (GENERATED ALWAYS AS IDENTITY, NULLS NOT DISTINCT)
  • Cleaner naming & documentation

Conclusion: pg-aiguide produces more robust, performant, maintainable schemas.

🚀 Quickstart

Agent Skills

Install curated PostgreSQL best-practice skills for your AI coding agent:

npx skills add timescale/pg-aiguide --skill postgres

Or install the schema exploration skill to investigate an existing PostgreSQL database:

npx skills add timescale/pg-aiguide --skill schema-exploration

Or pick individual skills interactively:

npx skills add timescale/pg-aiguide

Works with Claude Code, Cursor, Codex, Gemini CLI, VS Code, and 40+ other agents.

For even deeper PostgreSQL knowledge, also add the MCP server to give your agent semantic search over the official PostgreSQL, TimescaleDB, and PostGIS manuals.

MCP Server

For semantic search over PostgreSQL, TimescaleDB, and PostGIS documentation, add the public MCP server:

https://mcp.tigerdata.com/docs

Manual MCP configuration using JSON

{
  "mcpServers": {
    "pg-aiguide": {
      "url": "https://mcp.tigerdata.com/docs"
    }
  }
}

Or it can be used as a Claude Code Plugin:

claude plugin marketplace add timescale/pg-aiguide
claude plugin install pg@aiguide

Install by environment

Claude Code

This repo serves as a claude code marketplace plugin. To install, run:

claude plugin marketplace add timescale/pg-aiguide
claude plugin install pg@aiguide

This plugin uses the skills available in the skills directory as well as our publicly available MCP server endpoint hosted by TigerData for searching PostgreSQL documentation.

Codex

Run the following to add the MCP server to codex:

codex mcp add --url "https://mcp.tigerdata.com/docs" pg-aiguide

Cursor

One-click install:

Or add the following to .cursor/mcp.json

{
  "mcpServers": {
    "pg-aiguide": {
      "url": "https://mcp.tigerdata.com/docs"
    }
  }
}

Gemini CLI

Run the following to add the MCP server to Gemini CLI:

gemini mcp add -s user pg-aiguide "https://mcp.tigerdata.com/docs" -t http

OpenCode

Add the following to your global OpenCode config file (~/.config/opencode/opencode.json), or to a project level opencode.json. See the OpenCode config docs for more about file locations, and the MCP server docs for more about the mcp options.

{
  "$schema": "https://opencode.ai/config.json",
  "mcp": {
    "pg-aiguide": {
      "type": "remote",
      "url": "https://mcp.tigerdata.com/docs"
    }
  }
}

Add use pg-aiguide to your prompts to use the pg-aiguide tools.

Visual Studio

Click the button to install:

VS Code

Click the button to install:

Alternatively, run the following to add the MCP server to VS Code:

code --add-mcp '{"name":"pg-aiguide","type":"http","url":"https://mcp.tigerdata.com/docs"}'

VS Code Insiders

Click the button to install:

Alternatively, run the following to add the MCP server to VS Code Insiders:

code-insiders --add-mcp '{"name":"pg-aiguide","type":"http","url":"https://mcp.tigerdata.com/docs"}'

Windsurf

Add the following to ~/.codeium/windsurf/mcp_config.json

{
  "mcpServers": {
    "pg-aiguide": {
      "serverUrl": "https://mcp.tigerdata.com/docs"
    }
  }
}

💡 Your First Prompt

Once installed, pg-aiguide can answer Postgres questions or design schemas.

Simple schema example prompt

Create a Postgres table schema for storing usernames and unique email addresses.

Complex schema example prompt

You are a senior software engineer. You are given a task to generate a Postgres schema for an IoT device company. The devices collect environmental data on a factory floor. The data includes temperature, humidity, pressure, as the main data points as well as other measurements that vary from device to device. Each device has a unique id and a human-readable name. We want to record the time the data was collected as well. Analysis for recent data inclu

View source on GitHub