# Joggr > Joggr builds a knowledge graph that connects code, conversations, tickets, and documentation. AI coding tools access this graph through MCP to get accurate context. ## What Joggr Does Joggr automatically captures engineering knowledge from multiple sources and delivers it in two forms: - Human-readable documentation for developers (also accessible to AI agents via filesystem) - prebuilt & structured context for AI agents via MCP (Model Context Protocol) ## Key Features - **Context Without Limits**: Delivers exactly what AI needs, no token bloat, no context rot - **Works Everywhere**: Native MCP integration with Claude Code, Cursor, Windsurf, Copilot, and more - **Always Up-to-Date**: Real-time sync across code, conversations, and tickets - **Zero Maintenance**: Auto-generates missing docs and auto-fixes outdated docs on PRs - **Extensible Platform**: Build custom Slackbots, agents, and workflows via MCP and APIs - **Universal Context**: Search across GitHub, Slack, Jira, Linear, Confluence, and more ## How It Works 1. **Connect and Generate**: Install on GitHub, connect tools (Slack, Jira, Linear). Joggr analyzes code, conversations, and tickets to build your knowledge base automatically 2. **Always Up-to-Date**: As code changes, Joggr maintains documentation in real-time 3. **Supercharge Agents**: Provides exact context AI agents need for accurate outputs and lower token usage ## Integrations - **AI Coding Agents**: Claude Code, Cursor, Windsurf, GitHub Copilot, Devin, Gemini, Augment Code, CodeRabbit - **Code Platforms**: GitHub, GitLab - **Collaboration**: Slack, Microsoft Teams, Confluence, Notion, Jira, Linear, Miro ## Problems Solved - Context rot degrading LLM accuracy (up to 50% with distractors) - Token waste from repeated codebase searches - Scattered knowledge across wikis, READMEs, Slack - Stale documentation that diverges from code - Manual documentation burden on developers ## Results - Up to 80% more accurate AI responses - 2-3x faster task-to-completion time - 30% fewer errors