file-summarizer-mcp-server

# 📄 File Summarizer MCP Server A fully offline, multi-modal file summarization server built with FastMCP. Supports text, documents, audio, and video files with language detection, translation, and speech-to-text capabilities. ## 🚀 Features - 📂 **Reads multiple file types** — PDF, DOCX, TXT, audio (MP3, WAV), video (MP4, MOV) via Apache Tika & Whisper. - 🧠 **Summarizes file content or raw input text** automatically. - 🌐 **Multi-language support** — detects input language, translates to English if needed, or keeps summary in original language. - 🎙 **Speech-to-text transcription** — audio/video files transcribed via Whisper before summarization. - ⚙️ **Simple async MCP tools** — easy to extend and integrate with any MCP client. - 🔒 Fully offline capable — no need for external LLM APIs. - 🐍 Built with Python 3.12, FastMCP server framework, Apache Tika, Whisper, LangDetect, and Deep Translator. ## 🛠 Installation 1️⃣ Clone the repo: ```bash git clone https://github.com/Muskan244/File_Summarizer_MCP_Server.git cd File_Summarizer_MCP_Server ``` 2️⃣ Setup virtual environment: ```bash python3.12 -m venv .venv source .venv/bin/activate ``` 3️⃣ Install dependencies: ```bash pip install -r requirements.txt # Whisper requires ffmpeg installed system-wide brew install ffmpeg # (Mac) ``` ## 🔧 Usage Start the MCP server: ```bash uv run file_summarizer.py ``` It exposes the following tools: - `read_file(file_path)` - `summarize_file(file_path)` - `summarize_text(text)` - `detect_language(text)` - `translate_text(text)` - `transcribe_file(file_path)` You can invoke these via any MCP-compliant client (Claude Desktop, Open Interpreter, etc.).