# 🧠 Agentic Knowledge **Search any documentation as if you had written it yourself** An MCP server that guides AI assistants to navigate documentation using their built-in tools (grep, file reading) instead of traditional RAG. Leverages ever growing capabilities of large language models, better tool-calling and interpretation and agentic search patterns for precise, intelligent documentation discovery. --- ## 🎯 What Is This For? Give your AI assistant access to any documentation—yours or third-party—so it can find answers as naturally as you would. No embeddings, no vector databases, no complex infrastructure. **Perfect for:** - 📚 **Project documentation** - Your team's internal docs, APIs, guides - 🔧 **Framework references** - React, TypeScript, MCP SDK, any library - 🏢 **Enterprise knowledge** - Company wikis, architecture docs, runbooks - 🌐 **Open source projects** - Clone any repo's docs for instant access ## 🚀 Quick Start ### 1. Configure an MCP Client Add to your coding agent config something along the lines of ```json { "mcpServers": { "agentic-knowledge": { "command": "npx", "args": ["-y", "@codemcp/knowledge@latest"] } } } ``` ### 2. Set Up Your First Docset **Option A: Use the CLI (Recommended)** ```bash # For a Git repository npx @codemcp/knowledge create \ --preset git-repo \ --id react-docs \ --name "React Documentation" \ --url https://github.com/facebook/react.git # Initialize (downloads the docs) npx @codemcp/knowledge init react-docs # The MCP server starts automatically when Claude Desktop launches ``` **Option B: Manual Configuration** Create `.knowledge/config.yaml`: ```yaml version: "1.0" docsets: - id: my-docs name: My Project Documentation sources: - type: local_folder paths: ["./docs"] ``` ### 3. Use It Your AI assistant now has access to `search_docs` and `list_docsets` tools. Ask questions naturally: ``` "How do I implement a cleanup function in React useEffect?" "Show me the authentication setup in our docs" "Find examples of rate limiting in the API docs" ``` The assistant will receive intelligent navigation instructions and use grep/file reading to find the exact information. ## 📖 Documentation - **[User Guide](./USER_GUIDE.md)** - Detailed CLI commands, lifecycle, configuration - **[Examples](./examples/)** - Configuration examples and integration guides - **[Testing Guide](./TESTING.md)** - Comprehensive testing documentation ## 💡 How and Why It Works ### The Paradigm Shift Traditional RAG (Retrieval-Augmented Generation) was built for the **context-poor era** when models had 8K token limits. It: - Chunks documents (losing relationships) - Computes embeddings (missing precise terminology) - Retrieves fragments (losing context) - Requires massive infrastructure (vector DBs, rerankers) **Agentic Knowledge** leverages modern AI capabilities: - ✅ **200K+ token context windows** - Can read entire documentation sets - ✅ **Powerful filesystem tools** - grep, ripgrep, file reading built-in - ✅ **Intelligent navigation** - Provides search strategies, not fragments - ✅ **Zero infrastructure** - Just a config file and your docs ### From Retrieval to Navigation **Traditional RAG says:** _"Here are 50 fragments that mention your keywords"_ **Agentic Knowledge says:** _"Search for 'useState' in `./docs/react-18.2/hooks/`. If that doesn't help, try 'state management' in `./docs/patterns/`. Follow any 'See also' references you find."_ **The difference?** Guidance over fragments. Investigation over retrieval. ### How It Actually Works 1. **Configure docsets** - Point to local folders or Git repositories 2. **Initialize** - Downloads/symlinks documentation to `.knowledge/docsets/` 3. **MCP server** - Exposes `search_docs` and `list_docsets` tools 4. **AI searches** - Gets navigation instructions, uses grep/file tools 5. **Finds answers** - Reads complete documents with full context **Performance:** - **Setup**: Seconds (vs hours for RAG indexing) - **Response**: <10ms (vs 300-2000ms for RAG) - **Infrastructure**: None (vs Elasticsearch + Vector DB) - **Accuracy**: Complete context (vs fragment-based) ### Inspired By This approach is inspired by [The RAG Obituary](https://www.nicolasbustamante.com/p/the-rag-obituary-killed-by-agents) by Nicolas Bustamante and how Claude Code revolutionized code analysis by ditching RAG for direct filesystem exploration. ## 🚀 Local Development ```bash # Install dependencies pnpm install # Start development mode pnpm dev # Run tests pnpm test # Build all packages pnpm build ``` See [User Guide](./USER_GUIDE.md) for installation from source. ## 🤝 Contributing This project follows a structured development workflow. See our development documentation for contribution guidelines. ## 📄 License Distributed under the MIT License. See [`LICENSE`](./LICENSE) file for details. ---

🎯 Moving beyond RAG into the agentic era of knowledge systems

Inspired by The RAG Obituary by Nicolas Bustamante

knowledge

Installation

SKILL.md