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 Test Strategy?

Planning test coverage. Deciding what to test. Building CI/CD pipelines. Balancing test types

When should I NOT use Test Strategy?

Prototypes/throwaway code. Single-use scripts

Testing strategy critical

Test Strategy

Test strategy guidance covering test pyramid, testing trophy, what to test at each level, coverage targets, and practical approaches to building confidence.

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

Edit on GitHub Report Issue

Quick Reference

Test Strategy: Unit tests (70%) for pure logic/algorithms. Integration tests (20%) for boundaries/APIs. E2E tests (10%) for critical user journeys only. Test behavior, not implementation. More tests resemble real usage = more confidence. 80% coverage sweet spot. Static analysis catches type errors.

Use When

Planning test coverage
Deciding what to test
Building CI/CD pipelines
Balancing test types

Skip When

Prototypes/throwaway code
Single-use scripts

Test Strategy

Test strategy guidance covering test pyramid, testing trophy, what to test at each level, coverage targets, and practical approaches to building confidence.

Discussion

Code Review Patterns recommended Contract Testing Patterns recommended Load Testing Patterns recommended FastAPI Testing with Pytest critical

Test Strategy

Quick Reference

Use When

Skip When

Test Strategy

Discussion

Strictly Necessary

Analytics

Functional