/plugin marketplace add n24q02m/claude-pluginsThen /plugin install <name>@n24q02m-plugins. All 7 MCP servers in one marketplace.
| Server | Description | Agent Setup | Runtime |
|---|---|---|---|
| wet-mcp | Web search, content extraction, and documentation indexing | Guide | uvx wet-mcp |
| mnemo-mcp | Persistent AI memory with hybrid search and cross-machine sync | Guide | uvx mnemo-mcp |
| better-notion-mcp | Markdown-first Notion API with 10 composite tools | Guide | npx @n24q02m/better-notion-mcp |
| better-email-mcp | Email (IMAP/SMTP) with multi-account and auto-discovery | Guide | npx @n24q02m/better-email-mcp |
| better-godot-mcp | Godot Engine 4.x with 17 composite tools for scenes, scripts, and shaders | Guide | npx @n24q02m/better-godot-mcp |
| better-telegram-mcp | Telegram dual-mode (Bot API + MTProto) with 6 composite tools | Guide | uvx better-telegram-mcp |
| better-code-review-graph | Knowledge graph for token-efficient code reviews | Guide | uvx better-code-review-graph |
Setup any server: Copy the Agent Setup guide link and send it to your AI agent with "Please set up this MCP server for me."
These 8 principles are applied consistently across all 7 MCP servers and the relay infrastructure:
- Zero-Knowledge Relay -- E2E encryption (ECDH P-256 + AES-256-GCM). Relay server never sees plaintext credentials. URL fragment secrets stay client-side per RFC 3986.
- Composite Tool Pattern -- One tool per domain with action dispatch. 5-17 tools per server instead of 50+, saving LLM context tokens.
- 3-Tier Token Optimization -- Compact descriptions (always loaded), help docs (on demand), MCP resources (deep reference). ~77% token overhead reduction.
- Tool Annotations --
readOnlyHint,destructiveHint,idempotentHint,openWorldHintmetadata so the LLM knows tool behavior before calling. - Security Defense-in-Depth -- SSRF prevention, path traversal containment, XPIA boundary tags, error sanitization, rate limiting.
- Multi-User HTTP Mode -- Stateless DCR (HMAC-SHA256), per-user session isolation, AES-256-GCM credential encryption at rest, OAuth 2.1 + PKCE S256.
- Degraded Mode -- Server always starts, even without credentials. Help and config tools work. Data tools return setup instructions instead of crashing.
- Zero-Config Relay Setup -- Auto-open browser, user enters credentials, server receives config via encrypted relay, saves to local config.enc.
| Package | Description | Install |
|---|---|---|
| mcp-relay-core | Cross-language relay infrastructure for MCP servers (ECDH crypto, config storage, relay client) | npm i @n24q02m/mcp-relay-core / pip install mcp-relay-core |
| qwen3-embed | Lightweight ONNX inference for Qwen3 embedding and reranking models | pip install qwen3-embed |
| Tool | Description | Install |
|---|---|---|
| jules-task-archiver | Chrome Extension to bulk-archive completed Jules tasks | Download zip |
| modalcom-ai-workers | GPU-serverless AI workers on Modal.com (embedding, reranking, OCR, ASR) | -- |
Current -- KnowledgePrism: Knowledge intelligence platform with multi-model orchestration, RAG pipeline, and knowledge graph-assisted quality assurance across multilingual content.
Next -- Aiora: Health and environmental intelligence platform with deterministic rules, AQI pattern prediction, and real-time sensor data.
Vision (2027-2028) -- Graph World Model (Akasha): A paradigm shift from LLM-first to graph-first AI. The knowledge graph becomes the reasoning engine (symbolic rules + GNN inference), with the LLM reduced to a natural language translator. Four model tiers evolve progressively: Echo (LLM + RAG) -> Aura (enhanced graph reasoning) -> Nexus (hybrid symbolic + LLM) -> Akasha (full GWM, minimal LLM). Target: 10-25x cost reduction with full explainability and editability.