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 OpenTelemetry Tracing?

Microservices architecture. Distributed systems debugging. Request flow tracking. Performance optimization

When should I NOT use OpenTelemetry Tracing?

Single monolithic application. Simple scripts/CLIs. Logs-only monitoring

Observability tracing recommended

OpenTelemetry Tracing

OpenTelemetry distributed tracing patterns covering context propagation, instrumentation, sampling strategies, and production setup for TypeScript and Python.

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

OpenTelemetry Tracing: W3C Trace Context for propagation. Auto-instrument HTTP, DB, queues. Manual spans for business logic. Always close spans (try/finally). Semantic conventions for names. Head sampling for baseline, tail sampling for errors. Trace ID in logs for correlation. Export to Jaeger/Tempo/vendor.

Use When

Microservices architecture
Distributed systems debugging
Request flow tracking
Performance optimization

Skip When

Single monolithic application
Simple scripts/CLIs
Logs-only monitoring

OpenTelemetry Tracing

OpenTelemetry distributed tracing patterns covering context propagation, instrumentation, sampling strategies, and production setup for TypeScript and Python.

Discussion

Error Tracking Best Practices critical Metrics & Monitoring Best Practices critical Structured Logging Best Practices critical

OpenTelemetry Tracing

Quick Reference

Use When

Skip When

OpenTelemetry Tracing

Discussion

Strictly Necessary

Analytics

Functional