WaterCrawl MCP Server: Comprehensive Integration for AI Applications

Overview: What is WaterCrawl MCP Server?

WaterCrawl MCP Server serves as an essential bridge for integration between AI applications such as Claude Desktop, Continue, Cursor, and others, with data sources and tools through the Model Context Protocol (MCP). This protocol provides a standardized interface to enable seamless interaction, enhancing the capabilities of AI systems in various tasks such as web crawling, scraping, and search operations. By leveraging the WaterCrawl MCP Server, developers can streamline their application's functionality without the need for complex manual configurations.

🔧 Core Features & MCP Capabilities

The WaterCrawl MCP Server offers a wide range of features that cater to diverse AI workflows. It includes command-line tools and options for real-time monitoring, result download, and managing crawl or search requests. Examples of these capabilities are scrape-url, which allows scraping content from web pages with customizable settings; search, a tool for executing searches on the web using WaterCrawl’s extensive data sources; and download-sitemap for downloading sitemaps in different formats.

Real-time Monitoring

The server supports real-time monitoring of crawl and search requests, enabling developers to track progress and manage tasks efficiently. Using the monitor-request tool, users can monitor any in-progress operations with a defined timeout period and download results for further analysis.

⚙️ MCP Architecture & Protocol Implementation

Under the hood, WaterCrawl MCP Server is built on top of FastMCP, a high-performance framework designed for MCP implementation. The architecture includes multiple layers that handle incoming requests from AI clients, process them according to the configured settings, and finally deliver results back to the requesting parties.

The protocol flow can be visualized using a Mermaid diagram:

graph TD
    A[AI Application] -->|MCP Client| B[MCP Protocol]
    B --> C[MCP Server]
    C --> D[Data Source/Tool]
    style A fill:#e1f5fe
    style C fill:#f3e5f5
    style D fill:#e8f5e8

This diagram illustrates the communication flow between an AI application, MCP protocol, and ultimately a chosen data source or tool.

🚀 Getting Started with Installation

Quick Start via npx

For developers who wish to integrate WaterCrawl MCP Server without installing it locally, the easiest approach is using npx. This allows running the server directly from the terminal:

npx @watercrawl/mcp --api-key YOUR_API_KEY

Using with AI Assistants

WaterCrawl MCP Server can be integrated into various AI assistants like Codeium and Windsurf by configuring them with the appropriate command-line options.

Codeium/Windsurf Configuration Example:

To configure WaterCrawl MCP with Codeium or Windsurf, you would add the necessary settings to your assistant's configuration file:

{
  "mcpServers": {
    "watercrawl": {
      "command": "npx",
      "args": [
        "@watercrawl/mcp",
        "--api-key",
        "YOUR_API_KEY",
        "--base-url",
        "https://app.watercrawl.dev"
      ]
    }
  }
}

Claude Desktop Integration

Claude Desktop users can opt to run the watercrawl MCP server in SSE (Server-Sent Events) mode. This involves running a specific command:

npx @watercrawl/mcp sse --port 3000 --endpoint /sse --api-key YOUR_API_KEY

💡 Key Use Cases in AI Workflows

The WaterCrawl MCP Server enables developers to enhance their AI solutions by providing robust tools for data scraping, web searching, and managing crawl requests. Here are some typical use cases:

Real-World Example 1: Web Scraping for Market Intelligence

Suppose you're developing an AI application that needs to scrape market intelligence from various websites. Using the scrape-url tool within WaterCrawl MCP Server, your AI can easily extract data from URLs with customizable options like excluding certain tags or waiting times for content rendering.

Real-World Example 2: Automatic Fact Verification

Another use case could involve an AI application that verifies facts through web searches. By integrating the search tool via WaterCrawl MCP Server, your system can automatically send search queries and receive detailed results from WaterCrawl's vast database of crawled data.

🔌 Integration with MCP Clients

The compatibility matrix for WaterCrawl MCP shows that it supports a range of AI clients:

MCP Client	Resources	Tools	Prompts
Claude Desktop	✅	✅	✅
Continue	✅	✅	✅
Cursor	❌	✅	❌

This suggests that WaterCrawl MCP Server is fully compatible with tools and resources across various AI clients, ensuring a smooth integration process.

📊 Performance & Compatibility Matrix

The server is designed to handle high-traffic scenarios while maintaining consistent performance. Performance benchmarks include real-time monitoring capabilities and batch processing options for large-scale operations.

🛠️ Advanced Configuration & Security

The WaterCrawl MCP Server supports advanced configuration through environment variables, ensuring that users can tailor the setup according to their needs. Here’s an example of a .env file:

WATERCRAWL_BASE_URL=https://app.watercrawl.dev
WATERCRAWL_API_KEY=YOUR_API_KEY
SSE_PORT=3000                  # Optional, for SSE mode
SSE_ENDPOINT=/sse              # Optional, for SSE mode

❓ Frequently Asked Questions (FAQ)

Q1: Can I run WaterCrawl MCP Server in production?

Yes, the server is designed with robust features like real-time monitoring and batch processing capabilities.

Q2: What are the supported AI clients?

WaterCrawl supports several popular AI clients including Claude Desktop, Continue, and Cursor.

Q3: How do I integrate WaterCrawl MCP into my application?

You can integrate via npx or install it locally and configure your tool to use the server’s API keys and base URLs.

Q4: Can I monitor web crawls in real-time?

Yes, real-time monitoring is supported through the monitor-request tool, which allows you to track crawl operations.

Q5: What are the best practices for securing my MCP setup?

Ensure all environment variables, especially those containing API keys, are properly managed and not exposed publicly.

👨‍💻 Development & Contribution Guidelines

For developers interested in contributing to WaterCrawl MCP Server, follow these steps to get started:

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

🌐 MCP Ecosystem & Resources

The WaterCrawl MCP Server is part of a broader ecosystem that includes various tools and resources for AI developers. Explore our documentation and community forums to learn more about best practices, additional features, and support options.

---

By leveraging the WaterCrawl MCP Server, developers can significantly enhance their AI applications by providing them with robust web crawling, scraping, and search capabilities through a standardized interface.