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 either as:

• 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.

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• outdated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.

pg-aiguide is available as a public MCP server:

https://mcp.tigerdata.com/docs

{
  "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

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

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

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

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

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

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

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

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 includes finding outliers and anomalies based on measurements, as well as analyzing the data of particular devices for ad-hoc analysis. Historical data analysis includes analyzing the history of data for one device or getting statistics for all devices over long periods of time.

• search_docs Unified search tool supporting semantic (vector similarity) and keyword (BM25) search across multiple documentation sources:
  • postgres - Official PostgreSQL manual, scoped by version
  • tiger - Tiger Data's documentation (TimescaleDB and ecosystem)
  • postgis - PostGIS spatial extension documentation

• view_skill
  Exposes curated, opinionated PostgreSQL best-practice skills used automatically by AI coding assistants.

  These skills provide guidance on:

  • Schema design
  • Indexing strategies
  • Data types
  • Data integrity and constraints
  • Naming conventions
  • Performance tuning
  • Modern PostgreSQL features

Supported today:

• TimescaleDB (docs + skills)
• PostGIS (docs)

Coming soon:

• pgvector

We welcome contributions for additional extensions and tools.

See DEVELOPMENT.md for:

• running the MCP server locally
• adding new skills
• adding new docs

We welcome:

• new Postgres best-practice skills
• additional documentation corpora
• search quality improvements
• bug reports and feature ideas

Apache 2.0