Show HN: Setoku – Self-hosted knowledge server for AI agents
Setoku is an open-source, self-hosted MCP knowledge server that enables AI agents to query company data securely without running inference on the server. It implements a "context-first" architecture where agents must retrieve canonical metric definitions and business logic before executing read-only queries. The system features human-in-the-loop approval for knowledge updates, ensuring that AI proposals do not alter the underlying data semantics without human verification. It supports "vibe dash
Analysis
TL;DR
- Setoku is an open-source, self-hosted MCP knowledge server that enables AI agents to query company data securely without running inference on the server.
- It implements a "context-first" architecture where agents must retrieve canonical metric definitions and business logic before executing read-only queries.
- The system features human-in-the-loop approval for knowledge updates, ensuring that AI proposals do not alter the underlying data semantics without human verification.
- It supports "vibe dashboarding," allowing users to generate live, shareable web apps from natural language prompts backed by governed, read-only database access.
- Designed for low-cost deployment (single $5–12/mo VPS), it uses ClickHouse for the data lake and Postgres for the knowledge store, keeping production databases isolated.
Why It Matters
This solution addresses the critical challenge of grounding LLMs in proprietary enterprise data while maintaining strict security and governance boundaries. By decoupling the AI inference layer from the data layer through a standardized MCP interface, it allows organizations to leverage existing AI models without exposing sensitive credentials or risking unauthorized writes. This approach significantly reduces the barrier to entry for building secure, data-aware AI assistants within internal teams.
Technical Details
- Architecture: A self-hosted stack comprising a gateway, a knowledge store (Postgres), and a data lake (ClickHouse). It acts as a proxy between AI clients and internal data sources, ensuring no direct database exposure.
- MCP Tools: Provides three categories of tools: Context tools (
find_context,get_metric) for semantic understanding, Read-only query tools (run_query) for data retrieval with enforced SELECT-only constraints, and App tools (publish_app) for generating live dashboards. - Security & Governance: Implements row-level caps, statement timeouts, and append-only audit logs. Knowledge changes proposed by AI require human approval via an admin interface, preventing prompt injection from altering business logic.
- Integration: Works with any MCP-compatible client (Claude, Codex, etc.) and integrates via Claude Code skills for onboarding, connecting data sources, and curating knowledge.
- Deployment: Optimized for lightweight infrastructure, capable of running on a small VPS. Uses Docker for containerization and generates HTTPS certificates automatically during bootstrap.
Industry Insight
- Adoption of MCP Standards: As the Model Context Protocol gains traction, tools like Setoku demonstrate its utility in bridging the gap between unstructured AI interactions and structured enterprise data, encouraging broader adoption of standard interfaces.
- Shift to Human-in-the-Loop AI Ops: The requirement for human approval on knowledge updates highlights a growing industry trend toward hybrid automation, where AI suggests but humans validate, reducing hallucination risks in critical business contexts.
- Cost-Efficient AI Infrastructure: The ability to run complex data-aware AI applications on minimal hardware ($5–12/mo) challenges the assumption that enterprise-grade AI requires expensive cloud inference clusters, making it accessible for smaller teams and startups.
Disclaimer: The above content is generated by AI and is for reference only.