The mcp-summarizer is an MCP server designed to provide intelligent content summarization capabilities to AI models. Leveraging Google's Gemini 1.5 Pro, it generates concise summaries from various content types, including text, URLs, PDFs, EPUBs, and HTML, ensuring key information is preserved.

This server addresses the developer need for efficient content processing by offering customizable summary lengths, multi-language support, and smart context preservation. AI models can seamlessly interact with the mcp-summarizer through the summarize tool, specifying content, type, desired length, language, and style. The core value lies in its ability to distill large volumes of information into digestible summaries, enhancing AI model comprehension and response times. Integration is straightforward, requiring a simple configuration update in the client application to point to the server's endpoint. It uses standard input/output for communication, making it easy to integrate with existing systems.

Universal Content Summarization

The mcp-summarizer leverages Google's Gemini 1.5 Pro model to provide intelligent summarization capabilities across a wide range of content types. This core functionality allows AI models to quickly process and understand information from various sources, including plain text, web pages, PDF documents, EPUB books, and HTML content. The summarization process involves analyzing the input content, identifying key information, and generating a concise summary that captures the essence of the original material. This enables AI models to efficiently extract relevant information without needing to process the entire document, saving computational resources and time.

For example, an AI-powered research assistant could use mcp-summarizer to quickly summarize research papers in PDF format, allowing the AI to identify relevant studies and extract key findings for a literature review. Similarly, a content creation tool could use the summarizer to generate previews or summaries of web articles, providing users with a quick overview of the content before they decide to read the full article.

Customizable Summary Parameters

The mcp-summarizer offers several customizable parameters that allow users to tailor the summarization process to their specific needs. These parameters include maxLength, language, focus, and style. The maxLength parameter allows users to control the length of the generated summary, ensuring that it meets specific length requirements. The language parameter enables multi-language support, allowing users to summarize content in different languages. The focus parameter allows users to specify a particular aspect of the content to focus on in the summary, ensuring that the summary highlights the most relevant information. The style parameter allows users to choose the summary style, such as "concise," "detailed," or "bullet-points," providing flexibility in how the summary is presented.

For instance, a customer service chatbot could use the mcp-summarizer to summarize customer feedback in different languages, focusing on specific aspects such as product quality or customer service. The chatbot could then use this summarized information to identify common issues and improve customer satisfaction. Another use case could be a news aggregator that uses the summarizer to generate concise summaries of news articles, allowing users to quickly scan headlines and summaries to stay informed about current events. The style parameter could be used to generate bullet-point summaries for easy readability on mobile devices.

MCP Integration and Tooling

The mcp-summarizer is designed as an MCP server, enabling seamless integration with other MCP-compatible clients and tools. It exposes a summarize tool that can be invoked by other AI models or applications through the MCP protocol. This allows AI models to easily access summarization capabilities without needing to implement their own summarization logic. The server also includes a dynamic greeting resource as a basic example of MCP resource functionality. The integration information provided in the documentation allows developers to easily configure their desktop apps to use the mcp-summarizer server.

For example, an AI-powered writing assistant could use the mcp-summarizer to summarize existing text and then use the summarized content as a starting point for generating new content. The writing assistant could invoke the summarize tool on the mcp-summarizer server, passing the text to be summarized as a parameter. The server would then return a summary of the text, which the writing assistant could use to generate new content. This integration allows the writing assistant to leverage the summarization capabilities of the mcp-summarizer without needing to implement its own summarization logic.