open-index/open-npm huggingface dataset:
npm package: @iflow-mcp/scira-mcp
Description: An XMCP application with tool syntax
Latest version: 0.1.0

# Scira AI MCP Server

A Model Context Protocol (MCP) server that provides AI-powered search capabilities through the Scira AI platform. This server offers three specialized search tools for comprehensive information retrieval.

## Features

This MCP server provides the following tools:

### 🔍 Web Search (`search`)
- **Description**: Search the web for comprehensive information
- **Use Case**: Get real-time web search results with AI-powered analysis
- **Returns**: Structured search results with sources

### 👥 People Search (`people`)
- **Description**: Search for detailed people profiles and information
- **Use Case**: Find professional profiles, social media, and biographical data
- **Returns**: Name, social profiles (X, LinkedIn, GitHub), website, and bio

### 🐦 X Platform Search (`xsearch`)
- **Description**: Search for posts and content on X (formerly Twitter)
- **Use Case**: Find relevant tweets, discussions, and social media content
- **Parameters**: Query string and optional username filter
- **Returns**: X posts and content with sources

## Prerequisites

- Node.js 20.0.0 or higher
- A valid Scira AI API key

# How to use the MCP server

1. Put the MCP server url: https://mcp.scira.ai/mcp
2. Get the API Key from the Scira AI Platform(https://api.scira.ai)
3. Put the API Key in the MCP server config with the key `x-api-key`:
  ```json
  {
    "x-api-key": "your-api-key"
  }
  ```

4. You can use the MCP server in your Scira AI MCP client(https://mcpchat.scira.ai)


## Setup

1. Clone this repository
2. Install dependencies:
   ```bash
   pnpm install
   ```
3. Set up your Scira AI API key (the server expects it via `x-api-key` header or `Authorization: Bearer <token>`)

## Development

Run the development server:

```bash
pnpm dev
```

This starts the MCP server in development mode with hot reloading.

## Building for Production

Build the project:

```bash
pnpm build
```

## Running in Production

Start the HTTP server:

```bash
pnpm start
```

The server will run on port 3002 by default (configurable in `xmcp.config.ts`).

## Configuration

The server is configured via `xmcp.config.ts` with the following defaults:
- **Port**: 3002
- **CORS**: Enabled for all origins
- **Body Size Limit**: 10MB

## API Authentication

All tools require authentication through the Scira AI API. The server accepts API keys via:
- `x-api-key` header
- `Authorization: Bearer <token>` header

## Architecture

This project uses the xmcp framework with automatic tool discovery. Each tool is defined in the `src/tools/` directory with:
- **Schema**: Zod-based parameter validation
- **Metadata**: Tool description and annotations
- **Implementation**: Async function that calls Scira AI APIs

## Learn More

- [xmcp Documentation](https://xmcp.dev/docs)
- [Scira AI Platform](https://api.scira.ai)
npm package: @iflow-mcp/scrapezy-mcp
Description: A Model Context Protocol server for Scrapezy
Author: Scrapezy
Latest version: 0.1.1

# @scrapezy/mcp MCP Server

<a href="https://glama.ai/mcp/servers/rnktqsouvy">
  <img width="380" height="200" src="https://glama.ai/mcp/servers/rnktqsouvy/badge" alt="Scrapezy MCP server" />
</a>

[![smithery badge](https://smithery.ai/badge/@Scrapezy/mcp)](https://smithery.ai/server/@Scrapezy/mcp)

A Model Context Protocol server for [Scrapezy](https://scrapezy.com) that enables AI models to extract structured data from websites.

## Features

### Tools
- `extract_structured_data` - Extract structured data from a website
  - Takes URL and prompt as required parameters
  - Returns structured data extracted from the website based on the prompt
  - The prompt should clearly describe what data to extract from the website

## Installation

### Installing via Smithery

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

```bash
npx -y @smithery/cli install @Scrapezy/mcp --client claude
```

### Manual Installation
```bash
npm install -g @scrapezy/mcp
```

## Usage

### API Key Setup

There are two ways to provide your Scrapezy API key:

1. **Environment Variable:**
   ```bash
   export SCRAPEZY_API_KEY=your_api_key
   npx @scrapezy/mcp
   ```

2. **Command-line Argument:**
   ```bash
   npx @scrapezy/mcp --api-key=your_api_key
   ```

To use with Claude Desktop, add the server config:

On MacOS: `~/Library/Application Support/Claude/claude_desktop_config.json`
On Windows: `%APPDATA%/Claude/claude_desktop_config.json`

```json
{
  "mcpServers": {
    "scrapezy": {
      "command": "npx @scrapezy/mcp --api-key=your_api_key"
    }
  }
}
```

### Example Usage in Claude

You can use this tool in Claude with prompts like:

```
Please extract product information from this page: https://example.com/product
Extract the product name, price, description, and available colors.
```

Claude will use the MCP server to extract the requested structured data from the website.

### Debugging

Since MCP servers communicate over stdio, debugging can be challenging. We recommend using the [MCP Inspector](https://github.com/modelcontextprotocol/inspector), which is available as a package script:

```bash
npm run inspector
```

The Inspector will provide a URL to access debugging tools in your browser.

## License

MIT