MCP Content Summarizer Server: Smart Summaries & Scalable Insights

Transform complex content into精准summaries with MCP's enterprise AI server—effortlessly streamline workflows, boost productivity, and unlock actionable insights at scale.

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About MCP Content Summarizer Server

What is MCP Content Summarizer Server: Smart Summaries & Scalable Insights?

Imagine a legal team sifting through 50+ case documents in a single afternoon. The MCP Content Summarizer Server makes this possible by leveraging Google's Gemini 1.5 Pro to distill complex content into actionable insights. This cloud-native solution acts as a universal summary engine, handling everything from PDF contracts to live web articles with equal precision. Unlike basic keyword extractors, it maintains contextual relationships, ensuring summaries retain the original's intent and nuance.

How to use MCP Content Summarizer Server: Smart Summaries & Scalable Insights?

Developers can operationalize the server in three straightforward steps: clone the repository, build the TypeScript project, and initiate the service with pnpm start. Integration with desktop applications requires adding a single JSON configuration block specifying the server's execution path. For real-world usage, simply pass content parameters like this:

// Summarize a technical manual
server.invoke("summarize", {
  content: "base64_encoded_pdf",
  type: "pdf",
  focus: "regulatory requirements",
  style: "bullet-points"
})

MCP Content Summarizer Server Features

Key Features of MCP Content Summarizer Server: Smart Summaries & Scalable Insights?

Power users appreciate the adaptive summarization engine that:

Preserves technical terminology while simplifying jargon-heavy content
Supports multilingual processing (e.g., translating Spanish legal texts into concise Japanese summaries)
Enables style customization from boardroom-ready reports ("detailed") to quick-reference lists ("bullet-points")
Handles live data streams via URL inputs for real-time document analysis

Unique context retention ensures that even nested clauses in legal documents maintain their relational structure in output summaries.

Use Cases: Where MCP Transforms Workflows

Financial analysts use it to:

Automate quarterly report synopses from 100+ company filings
Track regulatory changes by summarizing updated compliance documents

Academic researchers leverage it to:

Create digestible overviews of 50+ research papers for literature reviews
Extract methodological frameworks from technical articles in multiple languages

Enterprise teams deploy it for:

Rapid contract comparison across regional offices
Automated customer inquiry categorization using website FAQ summaries

MCP Content Summarizer Server FAQ

FAQ: Answers to Common Questions

Q: How does it handle confidential documents?
A: All processing occurs in your private cloud instance, with optional encryption modules for sensitive data.

Q: Can it summarize live web content?
A: Yes - just provide a URL and specify "webpage" as the content type. It automatically ignores ads and navigation elements.

Q: What customization options exist?
A: Beyond standard parameters, developers can inject custom NLP pipelines via the API for domain-specific optimizations.

Q: Is there a free tier for testing?
A: The open-source core is freely available on GitHub, with enterprise features via subscription plans starting at $99/month.

Content

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.

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

Clone this repository
Install dependencies:

pnpm install
Build the project:

pnpm run build
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.