MCP Server Fetch TypeScript MCP Server: Universal Web Content Extraction and Conversion Tool

Overview: What is MCP Server Fetch TypeScript?

The MCP Server Fetch TypeScript serves as a versatile Model Context Protocol (MCP) server, enabling AI applications like Claude Desktop, Continue, Cursor, and others to interact with web content in highly structured ways. Unlike traditional scraping tools, this server offers precise control over the type of data retrieved—from raw text to fully rendered HTML or even Markdown—making it an indispensable tool for both data extraction and rendering tasks.

🔧 Core Features & MCP Capabilities

The MCP Server Fetch TypeScript is optimized to support diverse AI applications by providing a set of powerful tools under the Model Context Protocol. These include:

1. get_raw_text

• Description: This function retrieves raw text content directly from URLs.
• Usage: Best suited for scenarios where unprocessed, plain text data is needed from sources like JSON, XML, CSV, TSV files.
• Implementation Details: It works without any browser rendering to ensure fast and efficient data extraction. Users can focus on the core data without any interference.

2. get_rendered_html

• Description: This function fetches fully rendered HTML content from specified URLs.
• Usage: Ideal for modern web applications or single-page applications (SPAs) where post-rendering information is crucial, such as JavaScript-generated elements.
• Implementation Details: Utilizes Playwright to handle headless browser rendering. This ensures that dynamic content and interactive elements are properly captured.

3. get_markdown

• Description: Converts web content into well-formatted Markdown.
• Usage: Perfect for tasks involving content archiving, documentation, or generating structured text formats from various sources.
• Implementation Details: Preserves structural elements like tables, definition lists to maintain the integrity of the original data while converting it efficiently.

4. get_markdown_summary

• Description: Extracts and converts main content into clean Markdown.
• Usage: Useful for article or blog post extraction where navigation, headers, and footers need removal.
• Implementation Details: Automatically processes URLs to extract and format the key content sections, ensuring a concise yet informative summary.

⚙️ MCP Architecture & Protocol Implementation

The MCP Server Fetch TypeScript integrates seamlessly via the Model Context Protocol (MCP), which standardizes how AI applications interact with various data sources. By adhering to MCP, this server ensures compatibility and ease of integration across different platforms and environments:

Protocol Flow

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

Client Compatibility Matrix

MCP Client	Resources	Tools	Prompts	Status
Claude Desktop	✅	✅	✅	Full Support
Continue	✅	✅	✅	Full Support
Cursor	❌	✅	❌	Tools Only

🚀 Getting Started with Installation

The MCP Server Fetch TypeScript can be installed either globally or as a project dependency, making it easy to integrate into any development environment.

As a Global Package

npm install -g mcp-server-fetch-typescript

As a Project Dependency

npm install mcp-server-fetch-typescript

💡 Key Use Cases in AI Workflows

1. Content Extraction for Blogging: Automate the process of extracting main content from various websites and transforming it into blog posts.
2. Data Scraping for Research: Systematically gather specific data points or full pages, which can be used in research projects.

Real-world Implementation Example: Content Extraction for Blogs

Imagine a large-scale blogging platform that needs to automatically fetch content from thousands of articles online. Using MCP Server Fetch TypeScript, developers could set up periodic tasks to scrape articles, extract key sections, and format them into well-structured blog posts. This not only saves time but also ensures consistency in formatting across the entire platform.

Real-world Implementation Example: Data Scraping for Research

A data scientist needs to collect specific financial metrics from multiple websites daily. Instead of manually visiting each site, they can deploy MCP Server Fetch TypeScript as a cron job that runs at scheduled intervals, collecting and storing the necessary data in a structured format directly into their database.

🔌 Integration with MCP Clients

Configuration Code Sample

To add the MCP Server Fetch TypeScript to an AI application's configuration:

{
  "mcpServers": {
    "mcp-server-fetch-typescript": {
      "command": "npx",
      "args": ["-y", "mcp-server-fetch-typescript"],
      "env": {
        "API_KEY": "your-api-key"
      }
    }
  }
}

This configuration snippet ensures that the server is recognized and accessible to other MCP-compliant clients.

📊 Performance & Compatibility Matrix

The MCP Server Fetch TypeScript performs well across a wide range of web content types, ensuring seamless integration with various AI applications. Its compatibility matrix covers some of the most popular tools:

MCP Client	Resources	Tools	Prompts	Status
Claude Desktop	✅	✅	✅	Full Support
Continue	✅	✅	✅	Full Support
Cursor	❌	✅	❌	Tools Only

🛠️ Advanced Configuration & Security

To enhance the security and performance of MCP Server Fetch TypeScript, consider the following:

Using Environment Variables

Ensure sensitive information like API keys are stored securely using environment variables. Adjust the env section in your configuration to include any necessary credentials.

"mcpServers": {
  "mcp-server-fetch-typescript": {
    "command": "npx",
    "args": ["-y", "mcp-server-fetch(typescript)"],
    "env": {
      "API_KEY": process.env.API_KEY,
      "OTHER_CREDENTIALS": process.env.OTHER_CREDENTIALS
    }
  }
}

❓ Frequently Asked Questions (FAQ)

1. Q: How do I configure the MCP server for full support in my AI application?

   • A: Follow the configuration steps provided, ensuring all required environment variables are set.

2. Q: Can this server handle dynamic content effectively?

   • A: Yes, get_rendered_html leverages Playwright to fully render web pages, making it suitable for modern SPAs and other dynamically generated content.

3. Q: Is the data extracted by this server reliable for research purposes?

   • A: Absolutely, although specific use cases may require additional validation steps to ensure accuracy and integrity of the data.

4. Q: How do I debug issues with the MCP protocol interaction?

   • A: Use the @modelcontextprotocol/inspector tool to trace and analyze interactions between your AI application and the server.

5. Q: Are there plans to add more features in future updates?

   • A: Yes, ongoing development aims to expand capabilities such as video scrapping, interactive form handling, and more sophisticated rendering techniques.

👨‍💻 Development & Contribution Guidelines

Contributions are always welcome! Developers can submit pull requests or report issues via GitHub. Ensure your changes follow the existing coding standards and include tests where applicable.

1. Fork the Project

• Clone the repository: git clone https://github.com/tatn/mcp-server-fetch-typescript.git

2. Set Up the Environment

cd mcp-server-fetch-typescript
npm install

3. Run Development Mode

npm run dev

4. Create a Pull Request

• Submit your changes and ensure they pass all automated checks before creating a pull request.

🌐 MCP Ecosystem & Resources

The MCP Server Fetch TypeScript is part of the broader Model Context Protocol ecosystem, designed to facilitate seamless interactions between AI applications and external data sources. Explore more resources on the official Model Context Protocol website for additional tools and insights.

By leveraging this server, developers can significantly streamline their workflows and enhance their AI application’s capabilities through MCP integration.