What are the key benefits of using Document Analyzer?

Users can quickly extract deep insights from text, efficiently organize and manage large document collections, and perform intelligent semantic searches to find relevant information with ease.

What specific types of text analysis can it perform?

It performs dual-engine sentiment analysis (VADER + TextBlob), TF-IDF and frequency-based keyword extraction, and calculates multiple readability scores like Flesch, Flesch-Kincaid, and ARI.

What is Document Analyzer primarily used for?

Document Analyzer is designed for comprehensive text document analysis, offering insights into sentiment, keywords, and readability, alongside robust document management and semantic search capabilities.

How does Document Analyzer handle document storage and retrieval?

It uses persistent JSON-based storage for documents and their metadata, allowing for efficient management, tag-based organization, and powerful TF-IDF semantic similarity search.

Document Analyzer

Name: Document Analyzer
Author: Tathagat017

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Analyzes text documents for sentiment, keywords, and readability, while offering robust document management and semantic search capabilities.

This tool is a comprehensive document analysis server built with the FastMCP framework, designed to provide deep insights into text content. It offers advanced document analysis functionalities including dual-engine sentiment analysis (VADER + TextBlob), TF-IDF and frequency-based keyword extraction, and multiple readability scoring metrics (Flesch, Flesch-Kincaid, ARI). Beyond analysis, it provides powerful document management features such as JSON-based persistent storage, smart semantic search, and a tag system for organization. Leverages FastMCP for a simple setup, type safety with Pydantic, and multi-transport support (STDIO, HTTP, SSE), making it ideal for integrating document intelligence into various applications.

主要功能

01Multiple readability scoring metrics (Flesch, Flesch-Kincaid, ARI)

02Dual-engine sentiment analysis (VADER + TextBlob)

030 GitHub stars

04TF-IDF semantic similarity search for documents

05TF-IDF and frequency-based keyword extraction

06Persistent JSON-based document storage with metadata

使用案例

01Managing and searching large collections of documents with rich metadata

02Automating the extraction of insights and statistics from textual data

03Integrating comprehensive text analysis into applications via an API