DocsFetcher MCP Server: Universal Data Adapter for AI Applications

Overview: What is DocsFetcher MCP Server?

DocsFetcher is an MCP server that serves as a universal adapter for fetching and processing package documentation from multiple programming language ecosystems directly within AI applications like Claude Desktop, Continue, and Cursor. By integrating with the Model Context Protocol (MCP), DocsFetcher enables these applications to access comprehensive information without needing API keys or specific configurations.

🔧 Core Features & MCP Capabilities

DocsFetcher offers a wide range of functionalities that are essential for enhancing the capabilities of AI applications like Claude Desktop. Key features include:

• Supported Languages: Supports documentation fetching from multiple programming languages, including JavaScript, Python, Java, .NET, Ruby, PHP, Rust, Go, and Swift.
• Documentation Extraction: Automatically crawls documentation sites to extract comprehensive information such as READMEs, API documentation, code examples, and repository info.
• Structured Data: Provides structured data that is ideal for summarization by language models. This includes specialized prompts tailored specifically for documentation analysis.
• No API Key Required: Works natively with Claude Desktop, Continue, Cursor IDE, and other AI applications without the need for API keys.

MCP Protocol Implementation Details

DocsFetcher leverages the Model Context Protocol (MCP) to ensure seamless integration with various AI clients. By adhering to MCP standards, DocsFetcher can efficiently transfer structured data from its servers to the intended client application using a defined protocol flow and message format.

⚙️ MCP Architecture & Protocol Implementation

MCP Protocol Flow 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

MCP Client Compatibility Matrix

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

🚀 Getting Started with Installation

Claude Desktop Configuration

1. Open Claude Desktop → Settings → Developer
2. Click "Edit Config" and add the following configuration:

{
  "mcpServers": {
    "docsFetcher": {
      "command": "npx",
      "args": [
        "-y",
        "@smithery/cli@latest",
        "run",
        "@cdugo/mcp-get-docs",
        "--config",
        "'{}'"
      ]
    }
  }
}

Cursor IDE Configuration

1. Open Cursor IDE → Settings → MCP → Add New MCP Server
2. Add the following configuration:

{
  "Name": "docsFetcher",
  "Command": "npx -y @smithery/cli@latest run @cdugo/mcp-get-docs --config '{}'"
}

Prerequisites

Ensure that you have Node.js 18 or later installed on your system.

💡 Key Use Cases in AI Workflows

Basic Library Information

• Use Case: "What is Express.js and how do I use it?"
• Implementation: @fetch-package-docs with packageName='express' and language='javascript'

Multi-language Support

• Use Case: "Show me documentation for lodash in JavaScript"
• Implementation: @fetch-package-docs with packageName='lodash' and language='javascript'

🔌 Integration with MCP Clients

DocsFetcher is designed to work seamlessly with leading AI clients such as Claude Desktop, Continue, and Cursor IDE. It enables these applications to access package documentation by leveraging the Model Context Protocol (MCP). Users can configure integration through the respective application settings.

Example Configuration Code Sample

{
  "mcpServers": {
    "docsFetcher": {
      "command": "npx",
      "args": [
        "-y",
        "@smithery/cli@latest",
        "run",
        "@cdugo/mcp-get-docs",
        "--config",
        "'{}'"
      ]
    }
  }
}

📊 Performance & Compatibility Matrix

DocsFetcher maintains compatibility with a wide range of programming languages and AI applications. The server is optimized for efficient document fetching, making it an ideal choice for developers building complex AI workflows.

🛠️ Advanced Configuration & Security

Custom Port Configuration

To run the server on a custom port, use:

PORT=8080 npm start

Environmental Variables

Configure environmental variables to enhance security and functionality. For example:

{
  "env": {
    "API_KEY": "your-api-key"
  }
}

❓ Frequently Asked Questions (FAQ)

1. Why is there no API key required for DocFetcher MCP Server? DocsFetcher uses a standardized Model Context Protocol (MCP) which eliminates the need for traditional API keys, simplifying integration.

2. Does DocsFetcher support all languages uniformly? Yes, Documents are fetched from multiple language ecosystems like JavaScript, Python, Java, .NET, Ruby, PHP, Rust, Go, and Swift.

3. How do I configure DocsFetcher to work with Cursor IDE? Add the necessary configuration in Cursor IDE as shown above under Cursor IDE Configuration.

4. Can I use DocFetcher MCP Server for real-time data retrieval? While docsFetcher is optimized for static documentation fetching, it can be configured to handle dynamic updates with minor modifications to its protocol interactions.

5. What if a package does not have standard documentation? For non-standard packages, you can specify the URL directly or use specialized tools like @fetch-multilingual-docs to fetch from multiple language ecosystems.

👨‍💻 Development & Contribution Guidelines

To contribute to DocsFetcher MCP Server:

1. Fork the repository: https://github.com/cdugo/package-documentation-mcp
2. Clone it locally:

   git clone https://github.com/cdugo/package-documentation-mcp.git
   cd package-documentation-mcp
   

3. Install dependencies:

   npm install
   

For detailed development guidelines, refer to the README.md in the repository.

🌐 MCP Ecosystem & Resources

Explore more about Model Context Protocol (MCP) and other related resources on their official websites:

• Model Context Protocol Official Site
• DocsFetcher GitHub Repository

By integrating DocsFetcher MCP Server, developers can build robust AI workflows that seamlessly access comprehensive package documentation across multiple programming languages. This integration not only simplifies development but also ensures compatibility with leading AI applications and ecosystems.