open-index/open-npm huggingface dataset:
npm package: @mcpflow.io/mcp-everart
Description: 使用各种模型进行人工智能图像生成。
Author: Anthropic, PBC
Latest version: 1.0.2

# EverArt

<!-- MCPFlow Packaged MCP -->
> 此包由 [MCPFlow](https://mcpflow.io) 打包并发布到npm仓库。

使用各种模型进行人工智能图像生成。

## 安装与使用

直接使用npx运行:

```bash
npx @mcpflow.io/mcp-everart
```

或者先安装后使用:

```bash
# 安装
npm install @mcpflow.io/mcp-everart

# 使用
npx @mcpflow.io/mcp-everart
```

## 使用方法

请参考原始仓库的使用说明。

## 工具函数

### generate_image

**参数:**




## 原始信息

- **开发者:** modelcontextprotocol
- **版本:** 1.0.0
- **许可证:** MIT License
- **原始仓库:** [modelcontextprotocol/servers/tree/main/src/everart](https://github.com/modelcontextprotocol/servers/tree/main/src/everart)

## 原始README

# EverArt MCP Server

Image generation server for Claude Desktop using EverArt's API.

## Install
```bash
npm install
export EVERART_API_KEY=your_key_here
```

## Config
Add to Claude Desktop config:

### Docker
```json
{
  "mcpServers": {
    "everart": {
      "command": "docker",
      "args": ["run", "-i", "--rm", "-e", "EVERART_API_KEY", "mcp/everart"],
      "env": {
        "EVERART_API_KEY": "your_key_here"
      }
    }
  }
}
```

### NPX

```json
{
  "mcpServers": {
    "everart": {
      "command": "npx",
      "args": ["-y", "@modelcontextprotocol/server-everart"],
      "env": {
        "EVERART_API_KEY": "your_key_here"
      }
    }
  }
}
```

## Tools

### generate_image
Generates images with multiple model options. Opens result in browser and returns URL.

Parameters:
```typescript
{
  prompt: string,       // Image description
  model?: string,       // Model ID (default: "207910310772879360")
  image_count?: number  // Number of images (default: 1)
}
```

Models:
- 5000: FLUX1.1 (standard)
- 9000: FLUX1.1-ultra
- 6000: SD3.5
- 7000: Recraft-Real
- 8000: Recraft-Vector

All images generated at 1024x1024.

Sample usage:
```javascript
const result = await client.callTool({
  name: "generate_image",
  arguments: {
    prompt: "A cat sitting elegantly",
    model: "7000",
    image_count: 1
  }
});
```

Response format:
```
Image generated successfully!
The image has been opened in your default browser.

Generation details:
- Model: 7000
- Prompt: "A cat sitting elegantly"
- Image URL: https://storage.googleapis.com/...

You can also click the URL above to view the image again.
```

## Building w/ Docker

```sh
docker build -t mcp/everart -f src/everart/Dockerfile . 
```
npm package: @mcpflow.io/mcp-exa-mcp-server
Description: 使用 Exa API 的 AI 动力代码搜索 MCP 服务器，用于在 AI 助手中进行智能代码搜索和检索。
Author: it-beard
Latest version: 1.0.1

# Exa MCP Server

<!-- MCPFlow Packaged MCP -->
> 此包由 [MCPFlow](https://mcpflow.io) 打包并发布到npm仓库。

使用 Exa API 的 AI 动力代码搜索 MCP 服务器，用于在 AI 助手中进行智能代码搜索和检索。

## 安装与使用

直接使用npx运行:

```bash
npx @mcpflow.io/mcp-exa-mcp-server
```

或者先安装后使用:

```bash
# 安装
npm install @mcpflow.io/mcp-exa-mcp-server

# 使用
npx @mcpflow.io/mcp-exa-mcp-server
```

## 使用方法

## Installation

1. Clone this repository:
```bash
git clone https://github.com/it-beard/exo-server.git
cd exa-server
```

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

3. Build the project:
```bash
npm run build
```

4. Configure your Exa API key in the MCP settings file (tested with Cline):
```json
{
  "mcpServers": {
    "exa": {
      "command": "node",
      "args": ["/path/to/exa-server/build/index.js"],
      "env": {
        "EXA_API_KEY": "your-api-key-here"
      }
    }
  }
}
```

## 工具函数

### search

**参数:**




## 原始信息

- **开发者:** it-beard
- **版本:** 1.0.0
- **许可证:** MIT License
- **原始仓库:** [it-beard/exa-server](https://github.com/it-beard/exa-server)

## 原始README

# Exa MCP Server

An MCP (Model Context Protocol) server that provides AI-powered code search capabilities using the Exa API.

## Features

- Perform AI-powered code searches using natural language queries
- Get relevant code examples and documentation
- Configurable number of search results
- JSON response format with rich metadata

## Installation

1. Clone this repository:
```bash
git clone https://github.com/it-beard/exo-server.git
cd exa-server
```

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

3. Build the project:
```bash
npm run build
```

4. Configure your Exa API key in the MCP settings file (tested with Cline):
```json
{
  "mcpServers": {
    "exa": {
      "command": "node",
      "args": ["/path/to/exa-server/build/index.js"],
      "env": {
        "EXA_API_KEY": "your-api-key-here"
      }
    }
  }
}
```

## Usage

The server provides the following tools and resources:

### Tools

#### search
Perform an AI-powered search using Exa API

Input Schema:
```json
{
  "query": "Search query",
  "numResults": 10
}
```

### Resources

- `exa://search/{query}` - Search results for a specific query
- `exa://last-search/result` - Results from the most recent search query

## Development

1. Make your changes in the `src` directory
2. Build the project:
```bash
npm run build
```
3. Test your changes by configuring the server in your MCP settings

## Contributing

1. Fork the repository
2. Create your feature branch (`git checkout -b feature/amazing-feature`)
3. Commit your changes (`git commit -m 'Add some 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](LICENSE) file for details.
npm package: @mcpflow.io/mcp-financial-analysis-mcp-server
Description: 一个用于金融分析的人类学模型上下文协议（MCP）服务器，集成了alphavantage.com和financialmodellingprep.com的API。
Author: kablewy
Latest version: 1.0.0

# Financial Analysis MCP Server

<!-- MCPFlow Packaged MCP -->
> 此包由 [MCPFlow](https://mcpflow.io) 打包并发布到npm仓库。

一个用于金融分析的人类学模型上下文协议（MCP）服务器，集成了alphavantage.com和financialmodellingprep.com的API。

## 安装与使用

直接使用npx运行:

```bash
npx @mcpflow.io/mcp-financial-analysis-mcp-server
```

或者先安装后使用:

```bash
# 安装
npm install @mcpflow.io/mcp-financial-analysis-mcp-server

# 使用
npx @mcpflow.io/mcp-financial-analysis-mcp-server
```

## 使用方法

请参考原始仓库的使用说明。

## 工具函数

### stock_price

Get real-time and historical stock price data from Alpha Vantage

**参数:**

- `symbol`: Stock ticker symbol
- `dataType`: Response data format
- `interval`: Time interval between data points
- `outputSize`: Amount of data to return (compact = last 100 points, full = all data)

### company_fundamentals

Get company fundamental data from Financial Modeling Prep

**参数:**

- `symbol`: Stock ticker symbol
- `metrics`: Array of fundamental metrics to retrieve



## 原始信息

- **开发者:** kablewy
- **版本:** 1.0.0
- **许可证:** MIT License
- **原始仓库:** [kablewy/financial-analysis-mcp-server](https://github.com/kablewy/financial-analysis-mcp-server)

## 原始README

# Financial Analysis MCP Server

An MCP server implementation for financial analysis using Alpha Vantage and Financial Modeling Prep APIs.

## Features

- Real-time and historical stock price data
- Company fundamental data including:
  - Company overview
  - Income statements
  - Balance sheets
  - Cash flow statements
  - Financial ratios

## Setup

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

2. Configure environment variables in `.env`:
```
ALPHA_VANTAGE_API_KEY=your_alpha_vantage_api_key_here
FMP_API_KEY=your_fmp_api_key_here
```

3. Build and run:
```bash
npm run build
npm start
```

## Available Tools

### stock_price
Get real-time and historical stock price data from Alpha Vantage

Parameters:
- symbol (required): Stock ticker symbol
- interval: Time interval ('1min', '5min', '15min', '30min', '60min', 'daily')
- outputSize: Amount of data ('compact', 'full')
- dataType: Response format ('json', 'csv')

### company_fundamentals
Get company fundamental data from Financial Modeling Prep

Parameters:
- symbol (required): Stock ticker symbol
- metrics: Array of metrics to retrieve ('overview', 'income', 'balance', 'cash', 'ratios')