open-index/open-npm huggingface dataset:
npm package: @iflow-mcp/tkoba1974_mcp-kroki
Description: A Model Context Protocol server for converting Mermaid diagrams to SVG using Kroki.io
Author: Takao
Latest version: 1.1.0

# MCP Kroki Server

An MCP (Model Context Protocol) server for converting Mermaid diagrams to SVG images using Kroki.io.

## Features

- Generate URLs for diagrams using Kroki.io
- Download diagrams as SVG, PNG, PDF, or JPEG files
- Support for multiple diagram formats:
  - Mermaid
  - PlantUML
  - Graphviz
  - And many more (see Kroki.io documentation)

## Installation

### local install

```bash
git clone https://github.com/tkoba1974/mcp-kroki.git
cd mcp-kroki
npm install
npm run build
```

## Usage

The server provides two main tools:

### 1. Generate Diagram URL

Generates a URL for a diagram using Kroki.io.

Parameters:
- `type`: The diagram type (e.g., "mermaid", "plantuml")
- `content`: The diagram content in the specified format
- `outputFormat` (optional): The output format (svg, png, pdf, jpeg, base64)

### 2. Download Diagram

Downloads a diagram to a local file.

Parameters:
- `type`: The diagram type (e.g., "mermaid", "plantuml")
- `content`: The diagram content in the specified format
- `outputPath`: The path where the diagram should be saved
- `outputFormat` (optional): The output format (svg, png, pdf, jpeg)
- `scale` (optional, number, default: 1.0): Scaling factor for the diagram dimensions. Currently only affects SVG output by attempting to modify width/height attributes. Minimum value is 0.1.

## Example

```javascript
// Generate a URL for a Mermaid diagram
const result = await callTool('generate_diagram_url', {
  type: 'mermaid',
  content: 'graph TD; A-->B; B-->C; C-->D;',
  outputFormat: 'svg'
});

// Download a Mermaid diagram
const result = await callTool('download_diagram', {
  type: 'mermaid',
  content: 'graph TD; A-->B; B-->C; C-->D;',
  outputPath: '/path/to/diagram.svg'
});
```

## How It Works

The server uses the Kroki.io API to convert diagrams. The diagram content is compressed and encoded before being sent to Kroki.io.

## Usage with Claude Desktop

Add to your Claude Desktop configuration file (claude_desktop_config.json):

### local install:
```json
{
  "mcpServers": {
    "mcp-kroki": {
      "command": "node",
      "args": ["/path/to/mcp-kroki/build/index.js"]
    }
  }
}
```

### npx：

```json
{
  "mcpServers": {
    "mcp-kroki": {
      "command": "npx",
      "args": [
        "-y",
        "@tkoba1974/mcp-kroki"
      ]
    }
  }
}
```

## License

MIT
npm package: @iflow-mcp/tmhr1850_backlog-mcp-server
Description: Backlog APIへのアクセスを提供するMCPサーバー
Author: tomohiro1850
Latest version: 1.0.2

# Backlog MCP サーバー

Backlog API へのアクセスを提供する MCP サーバーです。プロジェクト管理、課題追跡、ファイル操作などの機能を利用できます。

## 機能

- **プロジェクト管理**: プロジェクト一覧の取得、詳細情報の閲覧
- **課題管理**: 課題の作成、更新、検索、コメント追加
- **ユーザー管理**: ユーザー情報の取得
- **ファイル操作**: 添付ファイルの管理
- **コメント機能**: 課題へのコメント追加
- **検索機能**: 課題やプロジェクトの検索

## ツール

### プロジェクト関連

#### getSpaces

- スペース情報を取得します
- 入力: なし
- 戻り値: スペース情報

#### getProjects

- プロジェクト一覧を取得します
- 入力: なし
- 戻り値: プロジェクト一覧

### 課題関連

#### getIssues

- 課題一覧を取得します
- 入力:
  - projectIdOrKey (string): プロジェクト ID またはキー
  - offset (number, オプション): 取得開始位置
  - count (number, オプション): 取得件数（最大 100）
  - sort (string, オプション): ソート順 ('created'または'updated')
  - order (string, オプション): ソート方向 ('asc'または'desc')
- 戻り値: 課題一覧

#### getIssue

- 課題の詳細情報を取得します
- 入力:
  - issueIdOrKey (string): 課題 ID またはキー
- 戻り値: 課題の詳細情報

#### createIssue

- 新しい課題を作成します
- 入力:
  - projectId (string/number): プロジェクト ID
  - summary (string): 課題の件名
  - issueTypeId (string/number): 課題種別 ID
  - priorityId (string/number, オプション): 優先度 ID
  - description (string, オプション): 課題の詳細
  - startDate (string, オプション): 開始日 (YYYY-MM-DD 形式)
  - dueDate (string, オプション): 期限日 (YYYY-MM-DD 形式)
  - assigneeId (string/number, オプション): 担当者 ID
- 戻り値: 作成された課題の情報

#### updateIssue

- 既存の課題を更新します
- 入力:
  - issueIdOrKey (string): 課題 ID またはキー
  - summary (string, オプション): 課題の件名
  - description (string, オプション): 課題の詳細
  - statusId (string/number, オプション): 状態 ID
  - priorityId (string/number, オプション): 優先度 ID
  - assigneeId (string/number, オプション): 担当者 ID
  - startDate (string, オプション): 開始日 (YYYY-MM-DD 形式)
  - dueDate (string, オプション): 期限日 (YYYY-MM-DD 形式)
- 戻り値: 更新された課題の情報

#### addComment

- 課題にコメントを追加します
- 入力:
  - issueIdOrKey (string): 課題 ID またはキー
  - content (string): コメント内容
- 戻り値: 追加されたコメントの情報

### 検索関連

#### searchIssues

- 課題を検索します
- 入力:
  - keyword (string): 検索キーワード
  - projectIdOrKey (string, オプション): プロジェクト ID またはキー
  - offset (number, オプション): 取得開始位置
  - count (number, オプション): 取得件数（最大 100）
- 戻り値: 検索結果の課題一覧

## 検索構文

### 課題検索

- `keyword`: フリーテキスト検索
- `projectId`: 特定のプロジェクトで絞り込み
- `statusId`: 状態で絞り込み
- `assigneeId`: 担当者で絞り込み

例: `keyword=バグ statusId=1 assigneeId=12345`

## セットアップ

### API キーの取得

1. Backlog にログイン
2. 個人設定 > API > API キーの発行
3. 発行された API キーをコピー

### 環境変数の設定

`.env`ファイルに以下の設定を追加します:

```
BACKLOG_DOMAIN=your-domain.backlog.com
BACKLOG_API_KEY=your-api-key

# テスト用設定（オプション）
TEST_PROJECT_ID=your-test-project-id
TEST_PROJECT_KEY=your-test-project-key
```

### Claude Desktop での使用

Claude Desktop で使用するには、`claude_desktop_config.json`に以下を追加します:

#### NPX の場合

```json
{
  "mcpServers": {
    "backlog": {
      "command": "npx",
      "args": ["-y", "@tmhr1850/backlog-mcp-server"],
      "env": {
        "BACKLOG_DOMAIN": "your-domain.backlog.com",
        "BACKLOG_API_KEY": "your-api-key"
      }
    }
  }
}
```

### ローカル実行

```bash
# ビルド
npm run build

# 実行
npm start
```

## テスト

```bash
# 全テスト実行
npm test

# 監視モードでテスト実行
npm run test:watch

# カバレッジレポート付きでテスト実行
npm run test:coverage
```

## ライセンス

この MCP サーバーは MIT ライセンスの下で提供されています。詳細はプロジェクトリポジトリの LICENSE ファイルをご覧ください。
npm package: @iflow-mcp/tmux-mcp
Description: MCP Server for interfacing with tmux sessions
Author: nickgnd
Latest version: 0.2.2

# Tmux MCP Server

Model Context Protocol server that enables Claude Desktop to interact with and view tmux session content. This integration allows AI assistants to read from, control, and observe your terminal sessions.

## Features

- List and search tmux sessions
- View and navigate tmux windows and panes
- Capture and expose terminal content from any pane
- Execute commands in tmux panes and retrieve results (use it at your own risk ⚠️)
- Create new tmux sessions and windows
- Split panes horizontally or vertically with customizable sizes
- Kill tmux sessions, windows, and panes

Check out this short video to get excited!

</br>

[![youtube video](http://i.ytimg.com/vi/3W0pqRF1RS0/hqdefault.jpg)](https://www.youtube.com/watch?v=3W0pqRF1RS0)

## Prerequisites

- Node.js
- tmux installed and running

## Usage

### Configure Claude Desktop

Add this MCP server to your Claude Desktop configuration:

```json
"mcpServers": {
  "tmux": {
    "command": "npx",
    "args": ["-y", "tmux-mcp"]
  }
}
```

### MCP server options

You can optionally specify the command line shell you are using, if unspecified it defaults to `bash`

```json
"mcpServers": {
  "tmux": {
    "command": "npx",
    "args": ["-y", "tmux-mcp", "--shell-type=fish"]
  }
}
```

The MCP server needs to know the shell only when executing commands, to properly read its exit status.

## Available Resources

- `tmux://sessions` - List all tmux sessions
- `tmux://pane/{paneId}` - View content of a specific tmux pane
- `tmux://command/{commandId}/result` - Results from executed commands

## Available Tools

- `list-sessions` - List all active tmux sessions
- `find-session` - Find a tmux session by name
- `list-windows` - List windows in a tmux session
- `list-panes` - List panes in a tmux window
- `capture-pane` - Capture content from a tmux pane
- `create-session` - Create a new tmux session
- `create-window` - Create a new window in a tmux session
- `split-pane` - Split a tmux pane horizontally or vertically with optional size
- `kill-session` - Kill a tmux session by ID
- `kill-window` - Kill a tmux window by ID
- `kill-pane` - Kill a tmux pane by ID
- `execute-command` - Execute a command in a tmux pane
- `get-command-result` - Get the result of an executed command
npm package: @iflow-mcp/togetherai-image-server
Description: A Model Context Protocol server
Latest version: 0.1.0

# Together AI Image Server

An MCP server for generating images using Together AI's image generation models.

<a href="https://glama.ai/mcp/servers/2cwphjnpgw"><img width="380" height="200" src="https://glama.ai/mcp/servers/2cwphjnpgw/badge" alt="Together AI Image Server MCP server" /></a>

## Setup

1. Install dependencies:
```bash
npm install
```

2. Build the server:
```bash
npm run build
```

## Configuration

### 1. Together AI API Key

The server requires a Together AI API key. You can get one from [Together AI's platform](https://api.together.xyz/).

### 2. Cline Configuration

Add the server to your Cline MCP settings file:

For macOS/Linux: `~/Library/Application Support/Windsurf/User/globalStorage/saoudrizwan.claude-dev/settings/cline_mcp_settings.json`

```json
{
  "mcpServers": {
    "togetherai-image": {
      "command": "node",
      "args": ["/path/to/togetherai-image-server/build/index.js"],
      "env": {
        "TOGETHER_API_KEY": "your-api-key-here"
      },
      "disabled": false,
      "autoApprove": []
    }
  }
}
```

## Usage in Cline

The server provides a `generate_image` tool with the following parameters:

```typescript
{
  prompt: string;           // Required: Text description of the image to generate
  model?: string;          // Optional: Model to use (default: 'black-forest-labs/FLUX.1.1-pro')
  width?: number;          // Optional: Image width in pixels (default: 1024)
  height?: number;         // Optional: Image height in pixels (default: 768)
  steps?: number;         // Optional: Number of inference steps (default: 28)
  n?: number;             // Optional: Number of images to generate (default: 1)
  outputDir?: string;     // Optional: Directory where images will be saved (default: './output')
}
```

### Example Usage

You can use the server in Cline like this:

```
Generate an image of a cat and save it to my desktop:

<use_mcp_tool>
<server_name>togeherai-image</server_name>
<tool_name>generate_image</tool_name>
<arguments>
{
  "prompt": "A cute cat sitting on a windowsill",
  "outputDir": "/Users/username/Desktop/generated-images"
}
</arguments>
</use_mcp_tool>
```

### Output

The tool returns a JSON response containing:
- `filepath`: Full path to the saved image
- `filename`: Name of the saved image file
- `dimensions`: Original and final dimensions of the image

## Features

- Supports Together AI's latest FLUX.1.1-pro model
- Automatically resizes images if dimensions are below 256 pixels
- Maintains aspect ratio during resizing
- High-quality JPEG output (90% quality)
- Creates output directories recursively
- Returns detailed metadata including image dimensions
npm package: @iflow-mcp/tokenizin-agency-mcp-nativewind
Description: MCP server for transforming Tailwind components to NativeWind 4
Author: Your Name
Latest version: 1.0.0

# @modelcontextprotocol/server-nativewind

MCP server for transforming Tailwind components to NativeWind 4.

## Installation

```bash
npm install -g @modelcontextprotocol/server-nativewind
```

## Usage

### As a CLI tool
```bash
mcp-nativewind
```

### In Claude for Desktop

Add to your `claude_desktop_config.json`:
```json
{
  "mcpServers": {
    "nativewind-transformer": {
      "command": "npx",
      "args": ["-y", "@modelcontextprotocol/server-nativewind"]
    }
  }
}
```

## Available Tools

1. transform-component: Transform a single component
2. transform-directory: Transform all components in a directory
3. analyze-component: Analyze Tailwind usage

## Development

1. Clone the repository
2. Install dependencies: `npm install`
3. Build the project: `npm run build`
4. Run tests: `npm test`

## Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

## License

MIT
npm package: @iflow-mcp/tommybez-dbt-semantic-layer-mcp-server
Description: MCP server for using the DBT Semantic Layer API
Author: TommyBez
Latest version: 0.1.3

# dbt Semantic Layer MCP Server

[![smithery badge](https://smithery.ai/badge/@TommyBez/dbt-semantic-layer-mcp)](https://smithery.ai/server/@TommyBez/dbt-semantic-layer-mcp)

A Model-Connector-Presenter (MCP) server for seamlessly querying the dbt Semantic Layer through Claude Desktop and other compatible AI assistants.

## What is the dbt Semantic Layer?

The [dbt Semantic Layer](https://docs.getdbt.com/docs/use-dbt-semantic-layer/dbt-sl) is a powerful feature that allows you to define metrics once in your dbt project and reuse them consistently across your entire data stack. It provides:

- A single source of truth for business metrics
- Consistent metric definitions across all data tools
- Simplified access to complex metrics for all team members

## About This Project

This MCP server acts as a bridge between AI assistants (like Claude) and the dbt Semantic Layer, enabling you to:

- Query metrics directly through natural language conversations
- Explore available metrics and their definitions
- Analyze data with dimensional breakdowns and filters
- Visualize results within your AI assistant interface

## Features

- 🔍 **Metric Discovery**: Browse and search available metrics in your dbt Semantic Layer
- 📊 **Query Creation**: Generate and execute semantic queries through natural language
- 🧮 **Data Analysis**: Filter, group, and order metrics for deeper insights
- 📈 **Result Visualization**: Display query results in an easy-to-understand format

## Prerequisites

- A dbt Cloud account with Semantic Layer enabled
- API access to your dbt Cloud instance
- Node.js (v14 or later)

## Installation

### Via Smithery (Recommended)

The easiest way to install is via [Smithery](https://smithery.ai/server/@TommyBez/dbt-semantic-layer-mcp):

```bash
npx -y @smithery/cli install @TommyBez/dbt-semantic-layer-mcp --client claude
```

## Usage

Once installed and configured, you can interact with the dbt Semantic Layer directly from Claude Desktop:

1. Ask about available metrics: "What metrics are available in my dbt Semantic Layer?"
2. Query specific metrics: "Show me monthly revenue for the last quarter grouped by product category"
3. Analyze trends: "What's the week-over-week growth in user signups?"

## Troubleshooting

If you encounter issues:

- Verify your API credentials are correct
- Ensure your dbt Cloud project has Semantic Layer enabled
- Check that your metrics are properly defined in your dbt project

## Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

## License

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

## Acknowledgments

- [dbt Labs](https://www.getdbt.com/) for creating the dbt Semantic Layer
- [Smithery](https://smithery.ai/) for the MCP deployment platform
- [LiteMCP](https://github.com/wong2/litemcp) for the MCP development package
npm package: @iflow-mcp/tongchenglvxing-mcp-server
Description: 基于同程旅行网API接口二次封装的MCP Server服务，目前支持火车票查询和系统时间查询功能
Author: 爱海贼的无处不在
Latest version: 0.0.2

# 同程旅行火车票 MCP Server

非官方，基于同城旅行网的接口，实现的同程旅行网火车票的MCP Server

## Tools

1. `query_train_tickets_list`
   - 火车票车次列表查询功能，它可以帮助用户获取指定城市/车站的火车票信息，数据来源于同程旅行网火车票查询，根据用户提供的出发站出发城市、到达站到达城市、出发日期进行查询结果，返回出发站信息和到达站信息和座位票价信息
   
   - Input: 
     - `depStationName` (string): 出发火车站名称或者出发城市名称。
     - `arrStationName` (string): 目的地火车站名称或者到城市名称。
     - `depDate` (string): 出发日期，格式为yyyy-MM-dd。
     
   - Returns: 
   
     ```json
      {
             用户查询出发站:xxxxxx,
             用户查询到达站:xxxxx,
             车次列表: [
               是否当日到达:"是",
               出发火车站是否是经停站:"是经停站",
               出发火车站是否是始发站:"是始发站",
               车次号: xxxxx,
               出发车站名称:xxxxx	,
               出发日期: xxxx		,
               出发时间: xxxx,
     
               到达车站名称: xxxx	,
               到达日期: xxxx	,
               到达时间: xxxx,
             
               历时小时分钟: xxxxx,
               座位类型: [
                 类型: xxxxx,
                 价格: xxxx,
                 余票:xxxx
              ]
          ]
     }
     ```
 2. `get_current_system_time`
   - 当前时间查询功能，它可以帮助你获取当前系统日期时间是多少,返回格式为:yyyy-MM-dd hh:mm:ss，火车票查询时时间相关可以使用

   - Returns:

     返回字符串格式的数据日期时间。     

### NPX本地开发环境

```json
{
  "mcpServers": {
    "tongchenglvxing-mcp-server": {
      "command": "npx",
      "args": [
        "-y",
        "/Users/tongchenglvxing-mcp-server/dist/index.js"
      ]
    }
  }
}
```

### NPX发布环境

```json
{
  "mcpServers": {
    "tongchenglvxing-mcp-server": {
      "command": "npx",
      "args": [
        "-y",
        "@wuchubuzai/tongchenglvxing-mcp-server"
      ]
    }
  }
}
```