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kukapay/dao-proposals-mcp

by Various

An MCP server that aggregates live governance proposals from major DAOs.

K

MCP

kukapay/dao-proposals-mcp

Added 1 June 2026

Overview

An MCP server that aggregates live governance proposals from major DAOs. Built in Python with 2 GitHub stars, it provides a standardized interface for querying proposals across decentralized autonomous organizations. The server uses the Model Context Protocol to enable large language models and agents to access proposal data.

Best for

Best for
Developers building AI agents or tools that need real-time governance data from major DAOs

Use cases

  • Integrate live DAO proposal data into AI agents
  • Build a dashboard for governance activity across multiple DAOs
  • Query proposals from a single endpoint for analysis or automation

Notes

An MCP server that aggregates live governance proposals from major DAOs. Built in Python with 2 GitHub stars, it provides a standardized interface for querying proposals across decentralized autonomous organizations. The server uses the Model Context Protocol to enable large language models and agents to access proposal data.

2 stars on GitHub. Last updated 2025-08-02. Licensed MIT.

Use cases

  • Integrate live DAO proposal data into AI agents
  • Build a dashboard for governance activity across multiple DAOs
  • Query proposals from a single endpoint for analysis or automation

Pros

  • Simple MCP-based interface for LLMs and agents
  • Aggregates proposals from multiple major DAOs in one service
  • Lightweight Python server that is easy to deploy

Cons

  • Very low GitHub stars suggests limited adoption and testing
  • Only covers major DAOs and may not support custom or newer ones
  • Requires self-hosting the server and handling rate limits

Indexed from awesome-mcp-servers-punkpeye and enriched against its public facts.

Pros

  • Simple MCP-based interface for LLMs and agents
  • Aggregates proposals from multiple major DAOs in one service
  • Lightweight Python server that is easy to deploy

Cons

  • Very low GitHub stars suggests limited adoption and testing
  • Only covers major DAOs and may not support custom or newer ones
  • Requires self-hosting the server and handling rate limits