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Mcp Server Rabel

Project description 🧠 RABEL MCP Server Recidive Active Brain Environment Layer Local-first AI memory with semantic search, graph relations, and soft pipelines. Mem0 inspired, HumoticaOS evolved. By Jasper & Root AI from HumoticaOS 💙 🚀 Quick Start # Install pip install mcp-server-rabel # For full features (vector search) pip install mcp-server-rabel[full] # Add to Claude CLI claude mcp add rabel -- python -m mcp_server_rabel # Verify claude mcp list # rabel: ✓ Connected 🤔 What is RABEL? RABEL gives AI assistants persistent memory that works 100% locally. Before RABEL: AI: "Who is Storm?" → "I don't know, you haven't told me" After RABEL: You: "Remember: Storm is Jasper's 7-year-old son" AI: *saves to RABEL* Later... You: "Who is Storm?" AI: *searches RABEL* → "Storm is Jasper's 7-year-old son!" No cloud. No API keys. No data leaving your machine. 🛠️ Available Tools Tool Description rabel_hello Test if RABEL is working rabel_add_memory Add a memory (fact, experience, knowledge) rabel_search Semantic search through memories rabel_add_relation Add graph relation (A --rel--> B) rabel_get_relations Query the knowledge graph rabel_get_guidance Get soft pipeline hints (EN/NL) rabel_next_step What should I do next? rabel_stats Memory statistics 📖 Examples Adding Memories # Remember facts rabel_add_memory(content="Jasper is the founder of HumoticaOS", scope="user") rabel_add_memory(content="TIBET handles trust and provenance", scope="team") rabel_add_memory(content="Always validate input before processing", scope="agent") Searching Memories # Semantic search - ask questions naturally rabel_search(query="Who founded HumoticaOS?") # → Returns: "Jasper is the founder of HumoticaOS" rabel_search(query="What handles trust?") # → Returns: "TIBET handles trust and provenance" Knowledge Graph # Add relations rabel_add_relation(subject="Jasper", predicate="father_of", object="Storm") rabel_add_relation(subject="TIBET", predicate="part_of", object="HumoticaOS") rabel_add_relation(subject="RABEL", predicate="part_of", object="HumoticaOS") # Query relations rabel_get_relations(subject="Jasper") # → Jasper --father_of--> Storm rabel_get_relations(predicate="part_of") # → TIBET --part_of--> HumoticaOS # → RABEL --part_of--> HumoticaOS Soft Pipelines (Bilingual!) # Get guidance in English rabel_get_guidance(intent="solve_puzzle", lang="en") # → "Puzzle: Read → Analyze → Attempt → Verify → Document" # Get guidance in Dutch rabel_get_guidance(intent="solve_puzzle", lang="nl") # → "Puzzel: Lezen → Analyseren → Proberen → Verifiëren → Documenteren" # What's next? rabel_next_step(intent="solve_puzzle", completed=["read", "analyze"]) # → Suggested next step: "attempt" 🏗️ Architecture ┌─────────────────────────────────────────────────────────────┐ │ RABEL │ │ Recidive Active Brain Environment Layer │ ├─────────────────────────────────────────────────────────────┤ │ │ │ Memory Layer → Semantic facts with embeddings │ │ Graph Layer → Relations between entities │ │ Soft Pipelines → Guidance without enforcement (EN/NL) │ │ │ │ Storage: SQLite + sqlite-vec (optional) │ │ Embeddings: Ollama nomic-embed-text (optional) │ │ │ │ 100% LOCAL - Zero cloud dependencies │ │ │ └─────────────────────────────────────────────────────────────┘ Graceful Degradation RABEL works with minimal dependencies: Feature Without extras With [full] Text memories ✅ ✅ Text search ✅ (LIKE query) ✅ (semantic) Graph relations ✅ ✅ Soft pipelines ✅ ✅ Vector search ❌ ✅ Embeddings ❌ ✅ (Ollama) 🌍 Philosophy "LOKAAL EERST - het systeem MOET werken zonder internet" (LOCAL FIRST - the system MUST work without internet) RABEL is built on the belief that: Your data stays yours - No cloud, no tracking, no API keys Soft guidance beats hard rules - Pipelines suggest, not enforce Bilingual by default - Dutch & English, more coming Graceful degradation - Works with minimal deps, better with more 🙏 Credits Inspired by: Mem0 - Thank you for the architecture insights! We took their ideas and made them: 100% local-first Bilingual (EN/NL) With soft pipelines With graph relations 🏢 Part of HumoticaOS RABEL is part of a larger ecosystem: Package Purpose Status mcp-server-tibet Trust & Provenance ✅ Available mcp-server-rabel Memory & Knowledge ✅ Available mcp-server-betti Complexity Management 🔜 Coming 📞 Contact HumoticaOS Website: humotica.com GitHub: github.com/jaspertvdm 📜 License MIT License - One love, one fAmIly 💙 Built with love in Den Dolder, Netherlands By Jasper & Root AI - December 2025

Greb Mcp

GREB MCP Server Semantic code search for AI agents without indexing your codebase or storing any data. Fast and accurate. Available on npm (cheetah-greb) and PyPI (cheetah-greb). FEATURES - Natural Language Search: Describe what you're looking for in plain English - High-Precision Results: Smart ranking returns the most relevant code first - Works with Any MCP Client: Claude Desktop, Cursor, Windsurf, Cline, Kiro, and more - No Indexing Required: Search any codebase instantly without setup - Fast: Results in under 5 seconds even for large repositories INSTALLATION Install Greb globally using pip or npm. Python: pip install cheetah-greb Node.js: npm install -g cheetah-greb GET YOUR API KEY 1. Go to Dashboard > API Keys at https://grebmcp.com/dashboard/api-keys 2. Click "Create API Key" 3. Copy the key (starts with grb_) CONFIGURATION Add to your MCP client config (Cursor, Windsurf, Claude Desktop, Kiro, etc.): Python installation: { "mcpServers": { "greb-mcp": { "command": "greb-mcp", "env": { "GREB_API_KEY": "grb_your_api_key_here" } } } } Node.js installation: { "mcpServers": { "greb-mcp": { "command": "greb-mcp-js", "env": { "GREB_API_KEY": "grb_your_api_key_here" } } } } CLAUDE CODE SETUP Mac/Linux (Python): claude mcp add --transport stdio greb-mcp --env GREB_API_KEY=grb_your_api_key_here -- greb-mcp Windows PowerShell (Python): claude mcp add greb-mcp greb-mcp --transport stdio --env "GREB_API_KEY=grb_your_api_key_here" Mac/Linux (Node.js): claude mcp add --transport stdio greb-mcp --env GREB_API_KEY=grb_your_api_key_here -- greb-mcp-js Windows PowerShell (Node.js): claude mcp add greb-mcp greb-mcp-js --transport stdio --env "GREB_API_KEY=grb_your_api_key_here" TOOL: code_search Search code using natural language queries powered by AI. Parameters: - query (string, required): Natural language search query - keywords (object, required): Search configuration - keywords.primary_terms (string array, required): High-level semantic terms (e.g., "authentication", "database") - keywords.code_patterns (string array, optional): Literal code patterns to grep for - keywords.file_patterns (string array, required): File extensions to search (e.g., ["*.ts", "*.js"]) - keywords.intent (string, required): Brief description of what you're looking for - directory (string, required): Full absolute path to directory to search Example: { "query": "find authentication middleware", "keywords": { "primary_terms": ["authentication", "middleware", "jwt"], "code_patterns": ["authenticate(", "isAuthenticated"], "file_patterns": ["*.js", "*.ts"], "intent": "find auth middleware implementation" }, "directory": "/Users/dev/my-project" } Response includes: - File paths - Line numbers - Relevance scores - Code content - Reasoning for each match USAGE EXAMPLES Ask your AI assistant to search code naturally: "Use greb mcp to find authentication middleware" "Use greb mcp to find all API endpoints" "Use greb mcp to look for database connection setup" "Use greb mcp to find where user validation happens" "Use greb mcp to search for error handling patterns" LINKS Website: https://grebmcp.com Documentation: https://grebmcp.com/docs Get API Key: https://grebmcp.com/dashboard/api-keys