File Summarizer MCP Server

The File Summarizer MCP Server addresses a common bottleneck in AI-assisted development: quickly extracting meaningful insights from diverse file formats without manual preprocessing. By leveraging Apache Tika, the server can read PDFs, Word documents, plain text, HTML, JSON and many other file types with a single API call. Once the raw content is retrieved, it can be summarized or translated automatically, allowing developers to feed concise context directly into LLMs like Claude.

At its core, the server offers a small set of intuitive tools that fit neatly into existing MCP workflows. The tool pulls the full text from any supported file, while and generate concise summaries that reduce cognitive load for both humans and models. For multilingual documents, identifies the source language and converts it to English before summarization, ensuring that non‑English sources do not become a barrier. An optional tool extends support to audio and video files, turning spoken content into searchable text. All of these operations run asynchronously, preserving the responsiveness of client applications.

Developers can integrate this server with minimal friction. The FastMCP framework powers the backend, and a single configuration entry is all that’s needed to expose the tools in Claude Desktop or any other MCP‑compatible client. Because the server is published on PyPI, it can be installed with a single command and run in isolated virtual environments. The lightweight dependency set—Python 3.12, Apache Tika, langdetect, deep‑translator, and FastMCP—keeps the footprint small while delivering robust functionality.

Typical use cases include:

• Rapid document ingestion for research assistants, where a PDF or HTML report is summarized and fed to an LLM to generate questions or action items.
• Multilingual support for global teams, automatically translating and summarizing local documents before they reach the model.
• Audio‑to‑text workflows, such as transcribing meeting recordings and summarizing key takeaways for instant sharing.
• Automated content curation, where a batch of files is processed, summarized, and indexed for later retrieval.

By abstracting file parsing, language detection, translation, and summarization into a single MCP server, developers can focus on higher‑level logic while the server handles the heavy lifting of data preparation. Its modular, async design ensures smooth integration into existing AI pipelines and provides a scalable foundation for building more sophisticated document‑centric applications.