本周新增
0 星
近 3 周倍数
—
距上次推送
259 天
距上次发布
359 天
许可证
MIT

星标增长

每根柱子是这个周期记录下来的 star 增长:优先取逐个 stargazer 到达时间分桶的到达数,还没被 stargazer 扫描覆盖的仓库改用该周期的 star 净增;两个写者都没记录的周期画成虚线空档,而不是当成 0。柱高按各自窗口的峰值归一,两张图不共用坐标轴。指向任意一根柱子, tooltip 里是该周期的九项指标:当前值与周期内的净增。

指向或点击一根柱子,看当天的九项指标。

12/1712/301/111/242/62/193/33/16

2026 年 1 月 18 日 新增 0 个星标; 2026 年 2 月 1 日 新增 0 个星标; 2026 年 2 月 14 日 新增 0 个星标; 2026 年 2 月 16 日 新增 0 个星标; 2026 年 2 月 17 日 新增 0 个星标; 2026 年 2 月 21 日 新增 0 个星标; 2026 年 3 月 16 日 新增 0 个星标

证据时间轴

这个项目还没有记录到异动、发布或许可证变化。

README
The document stored for this repository, with its Chinese translation when one has been produced

Firefly

Firefly MCP Server

The Firefly MCP (Model Context Protocol) server is a TypeScript-based server that enables seamless integration with the Firefly platform. It allows you to discover, manage, and codify resources across your Cloud and SaaS accounts connected to Firefly.

Features

  • 🔍 Resource Discovery: Find any resource in your Cloud and SaaS accounts
  • 📝 Resource Codification: Convert discovered resources into Infrastructure as Code
  • 🔐 Secure Authentication: Uses FIREFLY_ACCESS_KEY and FIREFLY_SECRET_KEY for secure communication
  • 🚀 Easy Integration: Works seamlessly with Claude and Cursor

Prerequisites

  • Node.js (v14 or higher)
  • npm or yarn
  • Firefly account with generated access keys

Installation

You can run the Firefly MCP server directly using NPX:

npx @fireflyai/firefly-mcp

Environment Variables

You can provide your Firefly credentials in two ways:

  1. Using environment variables:
FIREFLY_ACCESS_KEY=your_access_key FIREFLY_SECRET_KEY=your_secret_key npx @fireflyai/firefly-mcp
  1. Using arguments:
npx @fireflyai/firefly-mcp --access-key your_access_key --secret-key your_secret_key

Usage

Stdio

Update the mcp.json file with the following:

{
  "mcpServers": {
    "firefly": {
      "command": "npx",
      "args": ["-y", "@fireflyai/firefly-mcp"],
      "env": {
        "FIREFLY_ACCESS_KEY": "your_access_key",
        "FIREFLY_SECRET_KEY": "your_secret_key"
      }
    }
  }
}

Run the MCP server using one of the methods above with the following command:

npx @fireflyai/firefly-mcp --sse --port 6001

Update the mcp.json file with the following:

{
  "mcpServers": {
    "firefly": {
      "url": "http://localhost:6001/sse"
    }
  }
}

Using with Cursor

  1. Start the MCP server using one of the methods above
  2. Use the Cursor extension to connect to the MCP server - see Cursor Model Context Protocol documentation
  3. Use natural language to query your resources

Example:

Prompt
Find all "ubuntu-prod" EC2 instance in 123456789012 AWS account and codify it into Terraform
Response
resource "aws_instance" "ubuntu-prod" {
  ami           = "ami-0c55b159cbfafe1f0"
  instance_type = "t3.micro"
}

Demo

https://github.com/user-attachments/assets/0986dff5-d433-4d82-9564-876b8215b61e

Contributing

  1. Fork the repository
  2. Create your feature branch (git checkout -b feature/amazing-feature)
  3. Commit your changes (git commit -m 'feat: Add amazing feature')
  4. Push to the branch (git push origin feature/amazing-feature)
  5. Open a Pull Request

License

This project is licensed under the MIT License - see the LICENSE file for details.

Support

For support, please visit Firefly's documentation or create an issue in this repository.

页面上的每个数字都来自记录下来的 stargazer 到达时间,没有评分公式,也没有 AI 判定。 任何结论都可以用 /api/v1/repos/{id}/stats 里的原始增量自己重算。