Erold is an AI-native project management platform built specifically for developers and AI agents to collaborate. It features MCP server integration, REST API, CLI tool, and a knowledge base that gives AI persistent memory across sessions.

How does Erold integrate with Claude Code?

Erold provides a native MCP (Model Context Protocol) server with 18 tools that Claude Code can use directly. This allows Claude to create tasks, update progress, access project knowledge, and manage your projects without any copy-paste.

Yes! Erold offers a free tier with 1 project and unlimited tasks. Pro is $5/month with pay-as-you-go pricing for extra resources. Only pay for what you use beyond included allocations.

What AI tools does Erold support?

Erold works with any AI tool that supports MCP or REST APIs, including Claude Code, Claude Desktop, Cursor, Windsurf, GitHub Copilot, and custom AI agents. The API-first design ensures compatibility with current and future AI tools.

What is MCP (Model Context Protocol)?

MCP (Model Context Protocol) is a standard protocol that allows AI assistants to interact with external tools and services. Erold's MCP server provides 18 tools for task management, knowledge base access, and project operations that AI assistants can use directly.

How is Erold different from Jira, Linear, or Asana?

Erold is built API-first for AI agents, not humans-first with APIs added later. Traditional tools require copy-paste to share context with AI. Erold lets AI assistants read tasks, update progress, and access project knowledge directly through MCP, API, or CLI.

What is the Knowledge Base feature?

The Knowledge Base is persistent memory for AI assistants. Store project context, coding standards, architecture decisions, and any information AI should remember across sessions. AI can read and write knowledge entries, making it smarter about your specific project over time.

Does Erold have a CLI tool?

Yes! The Yet CLI (npm install -g @erold/cli) provides full task management from your terminal. Create tasks, list projects, mark items done, and integrate with git workflows. Perfect for developers who prefer command-line tools.

When should I use PostgreSQL Patterns?

PostgreSQL databases. Query optimization. Schema design. JSONB usage. Performance tuning

When should I NOT use PostgreSQL Patterns?

NoSQL databases. Simple applications. Other SQL databases

Data postgresql recommended

PostgreSQL Patterns

PostgreSQL patterns covering indexing strategies, JSONB optimization, query performance, partitioning, and production best practices.

Difficulty: intermediate
Read time: 1 min read
Version: v1.0.0
Confidence: established
Last updated: Updated Jan 9, 2026

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Quick Reference

PostgreSQL: Use EXPLAIN ANALYZE before optimization. B-tree for equality/range, GIN for arrays/JSONB/full-text. Partial indexes for hot paths. JSONB: use @> with GIN index, promote hot keys to columns. VACUUM/ANALYZE regularly. Connection pooling required. Use CTEs for readability, lateral joins for row-dependent subqueries.

Use When

PostgreSQL databases
Query optimization
Schema design
JSONB usage
Performance tuning

Skip When

NoSQL databases
Simple applications
Other SQL databases

PostgreSQL Patterns

PostgreSQL patterns covering indexing strategies, JSONB optimization, query performance, partitioning, and production best practices.

Discussion

Database Migrations critical Database Query Optimization critical Prisma Best Practices recommended SQLAlchemy 2.0 Async Patterns critical

PostgreSQL Patterns

Quick Reference

Use When

Skip When

PostgreSQL Patterns

Discussion

Strictly Necessary

Analytics

Functional