Document Analyzer
Analyzes text documents for sentiment, keywords, and readability, while offering robust document management and semantic search capabilities.
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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.
Características Principales
- Multiple readability scoring metrics (Flesch, Flesch-Kincaid, ARI)
- Dual-engine sentiment analysis (VADER + TextBlob)
- 0 GitHub stars
- TF-IDF semantic similarity search for documents
- TF-IDF and frequency-based keyword extraction
- Persistent JSON-based document storage with metadata
Casos de Uso
- Managing and searching large collections of documents with rich metadata
- Automating the extraction of insights and statistics from textual data
- Integrating comprehensive text analysis into applications via an API