MCP Knowledge Service

MCP Knowledge Service

Enables semantic search and management of development knowledge including global rules, project documentation, and references through vector-based search using libSQL. Features Tailscale-secured access control and tools for searching, browsing, and organizing development resources across multiple channels.

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README

MCP Knowledge Service

MCP-based knowledge and rules suite for Tailscale networks with semantic search via libSQL vectors.

Features

  • MCP Tools: rules.search, project.search, refs.list, and more
  • Semantic Search: Vector-based search using libSQL with OpenAI embeddings
  • Multi-Channel: Support for multiple MCP channels (rules, projects, refs)
  • Tailscale Security: Identity-based access control via Tailscale Serve
  • Vector Database: libSQL with native vector support for ANN search

Quick Start

  1. Setup Environment:

    ./scripts/setup.sh
    
  2. Configure Environment: Edit .env with your configuration:

    LIBSQL_URL=file:./data/knowledge.db
    OPENAI_API_KEY=your-openai-api-key
    
  3. Development:

    npm run dev        # Start development server
    npm run build      # Build for production
    npm test          # Run tests
    npm run lint      # Lint code
    

Architecture

  • src/mcp/ - MCP server and tool implementations
  • src/db/ - Database schema, connections, and migrations
  • src/http/ - REST API endpoints for ingestion
  • src/auth/ - Tailscale identity and access control
  • src/utils/ - Shared utilities and helpers

MCP Tools

Rules Service

  • rules.search(q, k?, tags?) - Search global development rules
  • rules.get(id) - Get specific rule by ID
  • rules.tags() - List all available rule tags

Project Service

  • project.search(project, q, k?) - Search within project docs
  • project.browse(project, path?) - Browse project structure
  • project.contextPack(project, facets?) - Get curated context bundle

References Service

  • refs.list(tags?, limit?) - List references with optional tag filter
  • refs.add(title, url, note?, tags_csv?) - Add new reference
  • refs.findByTag(tag) - Find references by specific tag

Database Schema

The service uses libSQL with vector support:

  • rules_global - Global AI development rules with embeddings
  • project_docs - Project-specific documentation with embeddings
  • refs - Quick reference links and documentation
  • access_tiers - User access control and permissions
  • audit_log - Query audit trail and metrics

Development Status

See TODO.md for current development phases and tasks.

Related

  • Memory subsystem development: /home/ubuntu/mem
  • Design documentation: docs/memory-design.md

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