Explanation: OpenWiki & FastMCP Architecture

An in-depth explanation of the technical design, data flows, and reasoning behind OpenWiki and FastMCP integrations.

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High-level architecture

The DSOM knowledge layer bridges localised documents to active AI agents via two complementary components:

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Why pure Python OpenWiki?

Historically, OpenWiki implementations required heavy Node.js runtimes or cloud compiler keys. This design violated Sovereign AI Rule 27 (Zero-Binary Mandate). Replacing Node binaries with the pure Python openwiki_emulator.py provides:

• Zero Node.js dependencies: The script runs offline using standard Python library APIs and the pyyaml package.
• In-place self-healing: The emulator scans embedded diagrams, detects syntactical errors, degrades blocks gracefully, and heals them when fixed.

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FastMCP context routing

The FastMCP server (tools/mcp/server.py) acts as the primary API broker for AI engines. Rather than feeding the entire documentation codebase into the context window, the server enables Progressive Disclosure:

1. The AI reads condensed states (dsom://brain/state).
2. If further details are needed, the AI calls targeted tools (search_palace or search_openwiki).
3. Only the relevant context fragments are returned, reducing overall token usage.

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Deep State of Mind (DSOM) For My AI Protocol | Harisfazillah Jamel (LinuxMalaysia) | 2026-08-14 Standard: UK English | DBP-standard Bahasa Melayu Malaysia (Piawai) | GNU General Public License v3.0