Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@MCP Knowledge Servicesearch for rules about API rate limiting"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
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 moreSemantic 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
Setup Environment:
./scripts/setup.shConfigure Environment: Edit
.envwith your configuration:LIBSQL_URL=file:./data/knowledge.db OPENAI_API_KEY=your-openai-api-keyDevelopment:
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 implementationssrc/db/- Database schema, connections, and migrationssrc/http/- REST API endpoints for ingestionsrc/auth/- Tailscale identity and access controlsrc/utils/- Shared utilities and helpers
MCP Tools
Rules Service
rules.search(q, k?, tags?)- Search global development rulesrules.get(id)- Get specific rule by IDrules.tags()- List all available rule tags
Project Service
project.search(project, q, k?)- Search within project docsproject.browse(project, path?)- Browse project structureproject.contextPack(project, facets?)- Get curated context bundle
References Service
refs.list(tags?, limit?)- List references with optional tag filterrefs.add(title, url, note?, tags_csv?)- Add new referencerefs.findByTag(tag)- Find references by specific tag
Database Schema
The service uses libSQL with vector support:
rules_global- Global AI development rules with embeddingsproject_docs- Project-specific documentation with embeddingsrefs- Quick reference links and documentationaccess_tiers- User access control and permissionsaudit_log- Query audit trail and metrics
Development Status
See TODO.md for current development phases and tasks.
Related
Memory subsystem development:
/home/ubuntu/memDesign documentation:
docs/memory-design.md
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