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未来 MCP

用于与 Solana 上的 Futarchy 协议交互的服务器实现。

设置说明

  1. 克隆存储库:

git clone <repository-url> cd futarchy-mcp
  1. 安装依赖项:

npm install
  1. 配置 RPC URL:

    • 打开src/server.ts

    • 更新连接初始化中的RPC URL:GXP3

    • 您可以使用:

      • 主网: https://api.mainnet-beta.solana.com

      • 开发网: https://api.devnet.solana.com

      • 或者您自己的 RPC 提供程序 URL(仅适用于此)

  2. 启动开发服务器:

npm run dev

可用路线

DAO 路线

  • GET /daos - 获取所有 DAO

  • GET /daos/:id - 通过 ID 获取特定的 DAO

  • GET /daos/:id/proposals - 获取特定 DAO 的所有提案

  • POST /daos/:id/proposals - 为 DAO 创建新提案(目前尚未测试,因为 dao 创建路线不存在)

    • 身体:

      { "descriptionUrl": "string", "baseTokensToLP": "number", "quoteTokensToLP": "number" }

提案路线

  • GET /proposals/:id - 根据 ID 获取特定提案

测试

您可以使用 Postman 或 curl 等工具测试路由。服务器默认在端口 9000 上运行。

curl 命令示例:

# Get all DAOs curl http://localhost:9000/daos # Get a specific DAO curl http://localhost:9000/daos/<dao-address> # Get proposals for a DAO curl http://localhost:9000/daos/<dao-address>/proposals # Create a new proposal curl -X POST http://localhost:9000/daos/<dao-address>/proposals \ -H "Content-Type: application/json" \ -d '{ "descriptionUrl": "https://example.com/proposal", "baseTokensToLP": 1000, "quoteTokensToLP": 1000 }'

光标的 MCP 服务器

该项目还包括一个 MCP(模型上下文协议)服务器,允许 Cursor 通过自定义工具与 Futarchy 后端进行交互。

设置 MCP 服务器

  1. 运行安装脚本来安装依赖项、构建项目并配置 Cursor:

chmod +x setup.sh ./setup.sh
  1. 或者手动配置:

    • 安装依赖项并构建项目:GXP8

    • 打开或创建~/.cursor/mcp.json

    • 添加以下配置(根据需要调整路径):GXP9

在 Cursor 中使用 MCP 服务器

您可以在 Cursor 的聊天中使用以下工具:

  1. getDaos - 从 Futarchy 系统获取所有 DAO

  2. getDao - 根据 ID 获取特定的 DAO

  3. getProposals - 获取特定 DAO 的所有提案

  4. getProposal - 根据 ID 获取特定提案

  5. createProposal - 为 DAO 创建新提案

例如,在 Cursor 的聊天中,你可以说:

Use the getDaos tool to retrieve a list of all DAOs from the futarchy-routes backend.

有关 MCP 服务器的更多详细信息,请参阅src/mcp/README.md

情绪分析功能

Futarchy MCP 服务器现已包含一个情绪分析工具,可以分析来自 Discord 和 Twitter 数据中针对特定提案的情绪。此功能有助于了解社区对提案的情绪,并可用于做出更明智的决策。

工作原理

  1. 情绪分析工具从 Discord 和 Twitter 收集有关特定提案的数据。

  2. 它使用自然语言处理技术分析情绪。

  3. 它将情绪分为不同的类别(代币经济学、协议参数等)。

  4. 它根据分析生成摘要、要点和关注点。

  5. 结果以结构化的格式呈现,前端可以轻松使用。

示例响应

{ "proposalId": "F3hsZzWinRAHbr6CUxdkUFBCH8qNk6Mi9Zfu3PMX49BC", "sentimentScore": -0.8, "primaryCategory": "Tokenomics", "categories": [ { "name": "Tokenomics", "score": 0.4 }, { "name": "Protocol Upgrades", "score": 0.3 }, { "name": "Partnerships Integrations", "score": 0.2 }, { "name": "Protocol Parameters", "score": 0.1 } ], "summary": "The proposal to launch a new Horizon token for the Aave ecosystem has faced significant backlash from the community...", "keyPoints": [ "The proposed token launch is seen as unnecessary and potentially harmful to the Aave token and community.", "The revenue-sharing model is perceived as frontloaded and unfair, favoring early years when adoption and revenue may be low.", "There is a desire to maintain the Aave token as the primary governance and utility token for the ecosystem." ], "concerns": [ "Dilution of the Aave token's value and attention.", "Misalignment of incentives with the proposed revenue-sharing model.", "Creation of a separate entity that could compete with the Aave ecosystem.", "Lack of transparency and community involvement in the decision-making process." ], "sources": { "discord": true, "twitter": true } }

如何使用

您可以按如下方式在 MCP 服务器中使用情绪分析工具:

const result = await mcp_futarchy_routes_getProposalSentiment({ proposalId: "F3hsZzWinRAHbr6CUxdkUFBCH8qNk6Mi9Zfu3PMX49BC" });

这将返回指定提案的情绪分析。

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