🗳️ DAO Proposal Explainer

DAO Explainer is an AI-powered tool that summarizes governance proposals from Snapshot, Agora, or any JSON-based DAO backend into short, readable overviews — powered by the Model Context Protocol (MCP).

Built to improve voter comprehension, reduce governance fatigue, and enable explainability for agent-based DAOs.

🔗 Live Demo → Coming soon via Nexus

📄 Spec → Part of the MCP initiative at https://mcp.z1labs.ai

🌐 Roadmap → https://z1labs.ai/roadmap

🧠 What It Does:

• ⛓️ Fetches live DAO proposals from Snapshot or other API endpoints
• 📑 Parses raw metadata and structures proposal JSON for LLM input
• 🧾 Summarizes proposals using contextual AI (GPT-4 / Claude / local)
• 💾 Optionally stores summary context onchain via MCP (putContext)
• 🏷️ Attaches proposal hash for auditability and replay

Supports multi-model output (e.g., “TL;DR”, “Pros & Cons”, “Risks”).

🧩 Architecture

This repo includes:

• /parser — DAO metadata fetchers + normalizers (e.g., Snapshot, Tally, Agora)
• /agents — Prompt templates and model formatters
• /context — MCP bundler (model hash, manifest, CID)
• /contracts — Optional integration with ContextStore.sol
• /ui — Frontend components (React or embedded module)

Optional: You can run this headlessly as a CLI or microservice.

🌐 Integrations

Supported sources:

• Snapshot.org
• Agora
• Tally
• Custom DAOs (via JSON endpoint)

Pluggable format — any DAO proposal engine returning structured JSON can be used.

🧩 Use Cases:

• LLM voters in DAOs
• Human-readable governance summaries
• DAO newsletters & bots
• Onchain memory for governance tracking
• Governance badges & credentials (paired with Verifiable Context)