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mcp-summarizer

MCP.Pizza Chef: 0xshellming

The mcp-summarizer is an MCP server that delivers intelligent summarization across diverse content types including text, web pages, PDFs, EPUBs, and HTML. Powered by Google's Gemini 1.5 Pro model and 3MinTop technology, it generates concise, customizable summaries that retain essential information, enhancing content comprehension and learning efficiency.

Use This MCP server To

Generate concise summaries from lengthy PDF documents Summarize web page content for quick reading Create brief overviews of EPUB book chapters Produce customizable-length summaries for plain text inputs Extract key points from HTML content for reports Enable fast content digestion in educational apps Support multi-language summarization workflows

README

MCP Content Summarizer Server

A Model Context Protocol (MCP) server that provides intelligent summarization capabilities for various types of content using Google's Gemini 1.5 Pro model. This server can help you generate concise summaries while maintaining key information from different content formats.

MCP Content Summarizer Server

Powered by 3MinTop

The summarization service is powered by 3MinTop, an AI-powered reading tool that helps you understand a chapter's content in just three minutes. 3MinTop transforms complex content into clear summaries, making learning efficient and helping build lasting reading habits.

Features

  • Universal content summarization using Google's Gemini 1.5 Pro model
  • Support for multiple content types:
    • Plain text
    • Web pages
    • PDF documents
    • EPUB books
    • HTML content
  • Customizable summary length
  • Multi-language support
  • Smart context preservation
  • Dynamic greeting resource for testing

Getting Started

  1. Clone this repository

  2. Install dependencies:

    pnpm install
    
  3. Build the project:

    pnpm run build
    
  4. Start the server:

    pnpm start
    

Development

  • Use pnpm run dev to start the TypeScript compiler in watch mode
  • Modify src/index.ts to customize server behavior or add new tools

Usage with Desktop App

To integrate this server with a desktop app, add the following to your app's server configuration:

{
  "mcpServers": {
    "content-summarizer": {
      "command": "node",
      "args": [
        "{ABSOLUTE PATH TO FILE HERE}/dist/index.js"
      ]
    }
  }
}

Available Tools

summarize

Summarizes content from various sources using the following parameters:

  • content (string | object): The input content to summarize. Can be:
    • Text string
    • URL for web pages
    • Base64 encoded PDF
    • EPUB file content
  • type (string): Content type ("text", "url", "pdf", "epub")
  • maxLength (number, optional): Maximum length of the summary in characters (default: 200)
  • language (string, optional): Target language for the summary (default: "en")
  • focus (string, optional): Specific aspect to focus on in the summary
  • style (string, optional): Summary style ("concise", "detailed", "bullet-points")

Example usage:

// Summarize a webpage
const result = await server.invoke("summarize", {
  content: "https://example.com/article",
  type: "url",
  maxLength: 300,
  style: "bullet-points"
});

// Summarize a PDF document
const result = await server.invoke("summarize", {
  content: pdfBase64Content,
  type: "pdf",
  language: "zh",
  style: "detailed"
});

greeting

A dynamic resource that demonstrates basic MCP resource functionality:

  • URI format: greeting://{name}
  • Returns a greeting message with the provided name

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

License

This project is licensed under the MIT License - see the LICENSE file for details.

mcp-summarizer FAQ

How does mcp-summarizer handle different content formats?
It supports plain text, web pages, PDFs, EPUBs, and HTML content, converting each into concise summaries.
Can I customize the length of the summaries?
Yes, the server allows adjustable summary lengths to fit different user needs.
Which AI model powers the summarization?
The summarization is powered by Google's Gemini 1.5 Pro model, with support for other LLMs like OpenAI's GPT-4 and Anthropic's Claude.
Is the summarizer capable of multi-language support?
Yes, it supports summarization in multiple languages to accommodate diverse content.
How does mcp-summarizer integrate with other MCP components?
It acts as a server exposing summarization functionality that MCP clients can call to enrich workflows.
What types of applications benefit most from this server?
Educational tools, research platforms, content management systems, and reading apps gain significant value.
Does it require internet access to function?
Typically, yes, to access the Gemini 1.5 Pro model and other cloud-based LLMs for summarization.
How secure is the data processed by mcp-summarizer?
It follows MCP principles for secure, scoped, and observable interactions to protect user data.