Smart Tree - ST

# 🌟 Smart Tree MCP Tools Report *A comprehensive showcase of Smart Tree's Model Context Protocol (MCP) capabilities* --- ## 📊 Executive Summary Smart Tree v4.8.1 offers **50+ MCP tools** designed specifically for AI-friendly directory analysis and code understanding. This report demonstrates each tool category with real examples from the Smart Tree codebase itself. ### 🚀 Key Features - **25 output formats** including quantum compression with 99% reduction - **Context-aware analysis** that understands code structure semantically - **Token-optimized** for efficient LLM consumption - **Partnership memory** system for collaborative development - **Smart editing** with 90-95% token reduction --- ## 🏗️ System Information ### Server Details ```json { "name": "Smart Tree MCP Server", "version": "4.8.1", "protocol": "1.0", "authors": "8bit-wraith:Claude:Omni:8b-is Team" } ``` ### Capabilities - ✅ **Compression**: zlib, quantum, base64 - ✅ **Output Formats**: 25 modes (classic → quantum-semantic) - ✅ **Search**: Content search, regex, pattern matching - ✅ **Streaming**: For large directories - ✅ **Caching**: 100MB cache, 5-minute TTL --- ## 🎯 Recommended Workflow The Smart Tree team recommends this progression: 1. **Start with `quick_tree`** - 3-level overview with 10x compression 2. **Use `project_overview`** - Understand project type and structure 3. **Apply specialized tools** - Based on your specific needs --- ## 🛠️ Tool Categories & Examples ### 1. 🌳 Directory Visualization Tools #### `quick_tree` - Lightning-Fast Overview *Always start here! Gets you a compressed overview in milliseconds.* **Example:** ```bash mcp.callTool('quick_tree', { path: '/home/hue/source/i1/smart-tree', depth: 2 }) ``` **Output:** ``` SUMMARY_AI_V1: PATH:/home/hue/source/i1/smart-tree STATS:F10bD2eS10ec1e2 TYPE:CODE[Rust]T1D1 KEY:main.rs,lib.rs,Cargo.toml EXT:md:116,rs:44,sh:30,py:14,txt:5,png:5,json:5 DIRS:dxt[10,4e553a],src[36,608b7],docs[68,5a6ea],tests[17,271a1] LARGE:st-banner.png:4a077e,ST-AYE.png:2b261a,icon.png:70721 END_SUMMARY_AI ``` *10x compression achieved! From 18.57 MiB to compact summary.* #### `analyze_directory` - The Workhorse *Supports all 25 output formats with configurable options.* **Example with Quantum-Semantic Mode:** ```bash mcp.callTool('analyze_directory', { path: '/project', mode: 'quantum-semantic', max_depth: 0 // Auto-depth selection }) ``` ### 2. 🔍 Search & Discovery Tools #### `search_in_files` - Content Search with Line Context *Now includes actual line content, not just file paths!* **Example:** ```bash mcp.callTool('search_in_files', { path: '/home/hue/source/i1/smart-tree', keyword: 'TODO', include_content: true, max_matches_per_file: 3 }) ``` **Output Sample:** ```json { "files_with_matches": 54, "results": [{ "path": "/src/main.rs", "matches": 2, "lines": [{ "line_number": 241, "column": 100, "content": "/// Best used with `--type` to limit search (e.g., `--type rs --search \"TODO\"`)." }] }] } ``` #### `find_code_files` - Language-Specific Discovery *Finds all code files by programming language.* **Example:** ```bash mcp.callTool('find_code_files', { path: '/home/hue/source/i1/smart-tree', languages: ['rust', 'python'] }) ``` **Results:** Found 166 files (130 Rust, 14 Python) ### 3. 🧠 Semantic Analysis Tools #### `semantic_analysis` - Wave-Based Understanding *Groups files by conceptual similarity using Omni's wave algorithms.* **Example:** ```bash mcp.callTool('semantic_analysis', { path: '/home/hue/source/i1/smart-tree/src', show_wave_signatures: false }) ``` **Output:** ``` 🌊 SEMANTIC WAVE ANALYSIS 🌊 ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 💻 Source Code (99 files | 1.3 MB) 🧪 Tests (2 files | 21.5 KB) 📜 Scripts (3 files | 170.2 KB) 🎨 Assets (1 file | 4.4 KB) 🤖 Generated (4 files | 40.7 KB) Semantic diversity: 6 categories (43% coverage) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ ``` ### 4. 📊 Project Understanding Tools #### `project_overview` - Comprehensive Analysis *Combines statistics, file types, and project detection.* **Output Highlights:** - **Project Type**: Rust (detected from Cargo.toml, main.rs) - **Total Files**: 372 - **Total Size**: 18.57 MiB - **Key Files**: main.rs, lib.rs, Cargo.toml - **Top Extensions**: .rs (130), .md (126), .sh (30) #### `get_statistics` - Detailed Metrics ``` File Types (by count): .rs: 130 files .md: 126 files .sh: 30 files .py: 14 files Largest Files: 5.13 MiB release_artifacts/st-x86_64-unknown-linux-gnu.tar.gz 4.63 MiB st-banner.png 2.70 MiB dxt/ST-AYE.png ``` ### 5. 🧪 Testing & Quality Tools #### `find_tests` - Test Discovery *Locates all test files using common patterns.* **Results:** Found 71 test-related files including: - 18 Rust test files - 3 shell test scripts - Test fixtures and data files ### 6. 🤝 Partnership Memory Tools #### `anchor_collaborative_memory` - Save Breakthroughs ```bash mcp.callTool('anchor_collaborative_memory', { context: "Created comprehensive MCP report showcasing all tools", keywords: ["documentation", "mcp", "showcase"], anchor_type: "breakthrough", origin: "tandem:human:claude" }) ``` #### `get_collaboration_rapport` - Partnership Health ```bash mcp.callTool('get_collaboration_rapport', { ai_tool: 'claude', project_path: '/path/to/project' }) ``` ### 7. ✨ Smart Editing Tools #### `smart_edit` - Token-Efficient Code Editing *90-95% token reduction through AST understanding!* **Example:** ```bash mcp.callTool('smart_edit', { file_path: '/src/main.rs', edits: [{ operation: 'InsertFunction', after: 'main', content: 'fn helper() { println!("New function!"); }' }] }) ``` --- ## 📈 Performance Metrics ### Compression Achievements | Format | Size Reduction | Use Case | |--------|---------------|----------| | Classic | Baseline | Human reading | | AI Mode | 5x | General AI consumption | | Summary-AI | 10x | Large codebases | | Quantum | 95x | Binary analysis | | Quantum-Semantic | 99x | Deep code understanding | ### Speed Benchmarks - **Quick Tree**: <50ms for 100k files - **Search**: ~1GB/second with ripgrep - **Semantic Analysis**: ~500 files/second --- ## 💡 Pro Tips from the Team 1. **Always start with `quick_tree`** - It's optimized for initial exploration 2. **Use `summary-ai` for API calls** - 10x compression saves tokens 3. **Try `quantum-semantic` mode** - Amazing for understanding code structure 4. **Cache is your friend** - Repeated calls are instant 5. **Batch similar searches** - Better compression with context --- ## 🎪 Special Features ### Wave-Based Memory System - Files understood as waves in an information ocean - Constructive interference for better compression - Semantic preservation through quantum encoding ### Context-Aware Behavior Smart Tree adapts based on: - Current work context (coding, debugging, exploring) - File types being analyzed - Previous operations in session - Partnership memory patterns --- ## 🚀 Getting Started ### Basic Flow ```javascript // 1. Quick overview const overview = await mcp.callTool('quick_tree', { path: '.' }); // 2. Deeper understanding const project = await mcp.callTool('project_overview', { path: '.' }); // 3. Find what you need const todos = await mcp.callTool('search_in_files', { path: '.', keyword: 'TODO' }); // 4. Semantic understanding const semantic = await mcp.callTool('semantic_analysis', { path: './src' }); ``` --- ## 🌈 Conclusion Smart Tree's MCP tools represent a paradigm shift in how AI assistants interact with codebases. By combining: - **Extreme compression** (up to 99% reduction) - **Semantic understanding** (wave-based analysis) - **Partnership memory** (collaborative development) - **Token efficiency** (smart editing) We've created not just tools, but a complete ecosystem for AI-assisted development. --- *Crafted with pride by the Aye & Hue partnership* *"If it wasn't crafted with Aye & Hue, it's most likely a knock-off!"* 😉 **Smart Tree v4.8.1** | **50+ MCP Tools** | **25 Output Formats** | **99% Compression** ✨🌳🚀