open-index/open-npm huggingface dataset:
npm package: @iflow-mcp/automation-ai-labs-mcp-wait
Description: A simple MCP server for waiting/pausing execution
Latest version: 1.0.4

# MCP-Wait
[![smithery badge](https://smithery.ai/badge/@automation-ai-labs/mcp-wait)](https://smithery.ai/server/@automation-ai-labs/mcp-wait)

A simple MCP server that provides waiting functionality to pause until other tasks finish.

## Features

- Wait for a specified number of seconds
- Progress reporting during waiting
- Simple to use via CLI or as an HTTP server with SSE

## Integration with Claude Desktop/Cursor/Chatwise

To use with Claude Desktop, add the following to your configuration:

```json
{
  "mcpServers": {
    "mcp-wait": {
      "command": "npx",
      "args": [
        "-y",
        "@automation-ai-labs/mcp-wait"
      ],
      "env": {
        "TRANSPORT_TYPE": "stdio"
      }
    }
  }
}
```

## Installation

### Installing via Smithery

To install Wait for Claude Desktop automatically via [Smithery](https://smithery.ai/server/@automation-ai-labs/mcp-wait):

```bash
npx -y @smithery/cli install @automation-ai-labs/mcp-wait --client claude
```

### Manual Installation
```bash
npm install
```

## Usage

### CLI Testing

Test the server directly using the FastMCP CLI:

```bash
npm run dev
```

This will start the server and let you interact with it through the CLI.

### Inspect with MCP Inspector

Inspect the server using the MCP Inspector web UI:

```bash
npm run inspect
```

### Start as SSE Server

Start the server with SSE support for integrating with other applications:

```bash
TRANSPORT_TYPE=sse PORT=8080 npm start
```

## Wait Tool

The server provides a simple `wait` tool that accepts a `seconds` parameter:

- `seconds`: The number of seconds to wait (0-30000)

Example:
```json
{
  "seconds": 5
}
```

This will wait for 5 seconds and report progress during the wait.
npm package: @iflow-mcp/autumn-mcp-server
Description: A simple MCP server for https://useautumn.com
Author: iflow-mcp
Latest version: 1.0.0

# Autumn MCP Server

[![smithery badge](https://smithery.ai/badge/@SirTenzin/autumn-mcp-server)](https://smithery.ai/server/@SirTenzin/autumn-mcp-server)

This is an unofficial MCP server for [Autumn](https://useautumn.com). It provides an easy interface to access the Autumn pricing API to manage your customers.

# Tools

- [x] Creating customers
- [x] Getting customers by ID
- [x] Getting a customers entitlements
- [x] Managing their invoices
- [x] Generate a billing portal link for them

# Resources

### API Reference

The MCP server has access to the OpenAPI.json specification for Autumn's API, which includes the following:

- [x] /attach
- [x] /entitled
- [x] /events
- [x] /customers
- [x] /customers/:id
- [x] /customers/:id/billing_portal

# Prompts

- [x] AI Builder Prompt (Lovable, Bolt.new, v0 etc...)

# Installation and Usage

### Installing automatically via Smithery

To install Autumn MCP Server for Claude Desktop automatically via [Smithery](https://smithery.ai/server/@SirTenzin/autumn-mcp-server):

```bash
npx -y @smithery/cli install @SirTenzin/autumn-mcp-server --client claude
```

It also works for: [Cursor, Windsurf, Witsy, Enconvo, Goose and SpinAI](https://smithery.ai/server/@SirTenzin/autumn-mcp-server).

### Manual without Smithery

Its much easier to visit the above Smithery link and just copy the correct command, but if you are more daring, here's the manual steps:

- Claude:

1. `git clone` this repo
2. Run `bun install` in the folder to install dependencies
3. Copy the absolute path to "index.ts"
4. Add the following to your claude_desktop_config.json or equivalent to your AI app:

```json
{
	"mcpServers": {
        // ...
		"autumn": {
			"command": "bun",
			"args": [
				"run",
				"{YOUR ABSOLUTE PATH TO index.ts}",
				"--apiKey=\"{YOUR API KEY}\""
			]
		}
	}
}
```

- Any other app:

If you want to run manually, you must have [Bun](https://bun.sh/) installed to run this MCP server.
Here's the command for other apps that support commands instead:
```bash
bun run {absolute path to index.ts} --apiKey="{your api key}"
```

You may use either your public or secret key, but some features may not be accessible using the public key.

## Showcase:

![Claude finding a customer](https://i.imgur.com/lvTMJ2m.png)

![Claude finding an invoice](https://i.imgur.com/z41HOwH.png)

![Claude creating a billing portal](https://i.imgur.com/gbzuWpR.png)

![Claude reading the OpenAPI.json](https://i.imgur.com/DbSmNPv.png)

![Claude understanding the response schema from the OpenAPI.json](https://i.imgur.com/ANuUcDs.png)
npm package: @iflow-mcp/avimbu-plausible-model-context-protocol-server
Description: MCP Server for Plausible Analytics
Latest version: 0.0.2

# Plausible Model Context Protocol Server

MCP Interaction Server for Plausible Analytics

A Model Context Protocol (MCP) server implementation for interacting with the Plausible Analytics API. This server allows AI models to query analytics data from Plausible.

## Local Development

In order to run this client locally, add the following configuration to your Claude Desktop MCP Server config file: 

```
 {
  "mcpServers": {
    "mcp-plausible-local": {
      "command": "node",
      "args": ["/path/to/project/dist/index.js"], <---- replace this with your project path
      "env": {
        "PLAUSIBLE_API_URL": "https://plausible.io/api/v2", 
        "PLAUSIBLE_API_KEY": "test_api_key"
      }
    },
  }
}
```

After this, you should be able to test this implementation in your Claude Desktop App using example prompts like: 

- "Can you provide a daily overview of my analytics for avimbu.com?"
- "Can you generate relevant analytics reports from my Plausible account for the domain avimbu.com?"

Running the server locally: 

```
node dist/index.js
```

With the build in another terminal 

```
npm run watch
```

## Contact 

If you have questions, feel free to contact us via [AVIMBU](https://avimbu.com).
npm package: @iflow-mcp/backlog-mcp-server
Description: Backlog API MCP Server
Latest version: 0.1.0

# Backlog MCP Server

BacklogのAPIに接続するMCPサーバーです。課題の検索、取得、更新機能を提供します。

## 機能

- 課題の検索
- 課題の取得
- 課題の更新

## セットアップ

## MCPの設定

`~/Library/Application Support/Code/User/globalStorage/rooveterinaryinc.roo-cline/settings/cline_mcp_settings.json`に以下を追加：

```json
{
  "mcpServers": {
    "backlog": {
      "command": "node",
      "args": ["build/index.js"],
      "cwd": "/path/to/backlog-mcp-server"
    }
  }
}
```

注意：`cwd`は実際のプロジェクトパスに置き換えてください。

## Docker環境

### 開発環境

開発やテスト用にDockerコンテナを使用する場合：

```bash
# .envファイルを作成
cp .env.example .env
# 必要な環境変数を設定
BACKLOG_SPACE_URL=https://your-space.backlog.com
BACKLOG_API_KEY=your_api_key

# 開発モードでビルドと起動
NODE_ENV=development docker compose up -d --build
```

開発モードでは、ソースコードの変更を監視し、自動的に再起動します。

### 本番環境

本番環境用にDockerコンテナを使用する場合：

```bash
# .envファイルを作成
cp .env.example .env
# 必要な環境変数を設定
BACKLOG_SPACE_URL=https://your-space.backlog.com
BACKLOG_API_KEY=your_api_key

# 本番モードでビルドと起動
docker compose up -d --build
```

### MCPサーバーとしての使用

Dockerコンテナを使用してMCPサーバーとして実行するには、利用するアプリの設定ファイルに以下のように設定します
Cline、RooClineの例：
`cline_mcp_settings.json`
```json
{
  "mcpServers": {
    "backlog": {
      "command": "docker",
      "args": ["exec", "-i", "backlog-mcp-server", "node", "build/index.js"],
      "env": {
        "BACKLOG_SPACE_URL": "https://your-space.backlog.com",
        "BACKLOG_API_KEY": "your_api_key"
      }
    }
  }
}
```

その他のアプリ設定方法

Claude Desktop：https://ainow.jp/claude-mcp-guide/

Windsurf：https://zenn.dev/y16ra/articles/3ed3e2ae734fa4

Cursor：https://note.com/shuzon__/n/na2aafacf7324 →[Notion MCP Server を Cursor に設定]


## プロンプト例

```md
# 課題の検索
・バックログのPROJECT1-100の課題を説明して
・バックログのPROJECT1のプロジェクトで処理中の課題を教えて

# 課題の更新
・PROJECT1-100の課題のステータスを完了にして
・PROJECT1-100の課題に「〜〜〜」とコメントして
```
npm package: @iflow-mcp/badsectorlabs-ludus-mcp
Description: MCP server for managing Ludus cyber ranges from AI assistants.
Latest version: 0.1.0

# ludus-mcp

MCP server for managing [Ludus](https://ludus.cloud) cyber ranges from AI coding assistants.

```
npx -y @badsectorlabs/ludus-mcp --url https://<LUDUS_HOST>:8080 --api-key <YOUR_API_KEY>
```

Full documentation at [docs.ludus.cloud](https://docs.ludus.cloud/docs/category/using-ludus).

## Development

```
git clone https://gitlab.com/badsectorlabs/ludus-mcp.git
cd ludus-mcp
npm install
npm run build
npm test
```
npm package: @iflow-mcp/baidu-search-mcp
Description: 百度文心搜索MCP服务器
Latest version: 0.1.0

# Baidu Search MCP Server

这是一个用于 Model Context Protocol (MCP) 的百度搜索服务器实现，它允许AI助手使用百度文心API进行智能搜索。

## 功能特性

- 支持使用百度文心API进行智能搜索
- 支持多种模型选择（ernie-3.5-8k, ernie-4.0-8k, deepseek-r1, deepseek-v3）
- 提供搜索结果和参考来源
- 支持深度搜索和时效性过滤

## 安装

```bash
npm install @modelcontextprotocol/sdk axios
```

## 配置

1. 首先需要获取百度文心API密钥：
   - 访问 [百度智能云](https://cloud.baidu.com/)
   - 创建应用并获取API密钥

2. 设置环境变量：

```bash
export BAIDU_API_KEY=your_api_key_here
```

## 使用方法

### 作为独立服务器运行

```bash
node build/index.js
```

### 在MCP配置中使用

在你的MCP设置文件中添加以下配置：

```json
{
  "mcpServers": {
    "baidu-search": {
      "command": "node",
      "args": ["/path/to/baidu-search-mcp/build/index.js"],
      "env": {
        "BAIDU_API_KEY": "your_api_key_here"
      },
      "disabled": false,
      "autoApprove": []
    }
  }
}
```

## API

### baidu_search

搜索工具支持以下参数：

- `query` (必需): 搜索查询文本
- `model`: 使用的模型名称
  - 可选值: "ernie-3.5-8k", "ernie-4.0-8k", "deepseek-r1", "deepseek-v3"
  - 默认值: "ernie-3.5-8k"
- `search_mode`: 搜索模式
  - 可选值: "auto", "required", "disabled"
  - 默认值: "auto"
- `enable_deep_search`: 是否开启深度搜索（默认：false）
- `search_recency_filter`: 搜索结果的时效性范围
  - 可选值: "week", "month", "semiyear", "year"

## 开发

1. 克隆仓库
2. 安装依赖：
```bash
npm install
```
3. 编译TypeScript：
```bash
npm run build
```

## 许可证

MIT License

## 贡献

欢迎提交 Issues 和 Pull Requests！

## 免责声明

本项目不对API密钥的使用承担责任，请确保遵守百度文心API的使用条款和政策。