pg-aiguide: PostgreSQL Expertise for AI Coding
Supercharge AI coding tools with optimized PostgreSQL knowledge and best practices.
Evidence shows: The repository does not provide explicit permission models, user confirmation mechanisms, data flow transparency, sensitive data handling, dependency security audits, external effects documentation, rollback mechanisms, or source attribution. All trust criteria are unsupported, hence scored 0.
Evidence shows: README and package.json descriptions are consistent, both pointing to PostgreSQL documentation search and skills provision, self-consistency scored 2. Dependencies are listed in package.json but no availability guarantees or locked versions, dependency availability scored 1. No failure messages or error handling documentation, failure messages scored 0.
Evidence shows: README clearly identifies target audience (AI coding assistants) and multiple usage scenarios (Agent Skills, MCP server, Claude plugin), audience and scenarios scored 2. Capability boundaries partially described (e.g., supported extensions), but no explicit limitations, capability boundaries scored 1. Trigger precision not detailed, only mentions skills used automatically, trigger precision scored 1. Environment fit good, supports multiple IDEs and CLIs, environment fit scored 2.
Evidence shows: README structure clear, includes quickstart, features, development sections, information architecture scored 2. Installation instructions detailed, covering multiple environments, install notes scored 2. Naming stability not explicit, but version number exists, naming stability scored 1. Example prompts and video provided, examples and FAQ scored 2. Known limitations not explicitly listed, only mentions pgvector coming soon, known limitations scored 1. License is Apache-2.0, license scored 2. Version number exists but no changelog, versioning and changelog scored 1. Maintenance responsibility not explicit, but contribution guidelines exist, maintenance responsibility scored 1.
Evidence shows: Output usability high, provides search and skills, output usability scored 2. Marginal value evident, provides AI-optimized skills, marginal value scored 2. Cost-benefit not quantified, cost-benefit scored 1.
Evidence shows: Claims in README (e.g., 4x constraints) not backed by verifiable tests or data, claim traceability scored 1. Cross-source corroboration limited, relies on README only, cross-source corroboration scored 1. Facts and inferences not clearly separated, fact-inference separation scored 1.
- No permission model or user confirmation mechanism provided; evaluate carefully before use.
- Dependencies not pinned, posing supply chain risk.
- Claims (e.g., 4x constraints) lack verifiable evidence.
- No rollback mechanism or data flow transparency documentation.
What does this agent do, and when should you use it?
pg-aiguide is an MCP server and Claude Code plugin that helps AI coding assistants generate better PostgreSQL code. It provides semantic search across the official PostgreSQL manual (version-aware) and curated 'skills' covering schema design, indexing, data types, and more. It can be used as Agent Skills via npx, as a public MCP server, or as a Claude Code plugin. The project is licensed under Apache 2.0 and supports multiple AI tools including Claude Code, Cursor, Codex, and Gemini CLI.
pg-aiguide exposes MCP tools like search_docs for semantic and keyword search over PostgreSQL, TimescaleDB, and PostGIS documentation. It also provides view_skill to access curated best-practice skills. When installed, AI agents automatically use these tools to retrieve relevant docs and skills, resulting in SQL with more constraints, indexes, and modern features.
- Developers using Claude Code or Cursor who want to create robust PostgreSQL schemas for e-commerce or IoT applications.
- Developers using Codex or Gemini CLI who need quick access to PostgreSQL documentation without leaving the terminal.
- Teams integrating Visual Studio or VS Code and wanting MCP-based documentation directly in their IDE.
- Data engineers designing time-series databases with TimescaleDB and needing best practices for partitioning and indexing.
- Organizations standardizing AI development workflows and seeking a unified PostgreSQL knowledge base for all agents.
What are this agent's strengths and limitations?
- Version-aware semantic search over official PostgreSQL docs ensures up-to-date knowledge.
- Curated skills automate best practices, leading to dramatically better SQL (e.g., 4x more constraints, 55% more indexes).
- Pluggable across many AI tools via Agent Skills and MCP protocol, with one-click install for Cursor, VS Code, etc.
- Open-source (Apache 2.0) and extensible for additional extensions like pgvector.
- Public MCP server is hosted by TigerData, introducing a third-party dependency for hosted deployments.
- Installation requires Node.js and CLI commands, which may be a barrier for non-technical users.
- Documentation coverage is limited to PostgreSQL, TimescaleDB, and PostGIS; other extensions are not yet supported.
How do you install or deploy this agent?
For Agent Skills, run npx skills add timescale/pg-aiguide --skill postgres or npx skills add timescale/pg-aiguide for interactive selection. For MCP, add the URL https://mcp.tigerdata.com/docs to your MCP client configuration. For Claude Code plugin, run claude plugin marketplace add timescale/pg-aiguide and then claude plugin install pg@aiguide.
How do you use this agent?
After installation, simply ask your AI agent to design a schema or answer Postgres questions. For example, 'Create a Postgres table schema for storing usernames and unique email addresses.' The agent will automatically leverage pg-aiguide's documentation and skills to produce high-quality results.
How does this agent compare with similar options?
Compared to using AI coding tools without any PostgreSQL-specific enhancement, pg-aiguide provides a substantial quality boost in generated SQL, as evidenced by the repository's own demonstration.