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metadata

title: Intelligent Content Organizer MCP Agent
emoji: 😻
colorFrom: blue
colorTo: green
sdk: gradio
sdk_version: 5.32.0
app_file: app.py
tag: '-mcp-server-track -Agents-MCP-Hackathon'
pinned: false
license: mit

A powerful Model Context Protocol (MCP) server for intelligent content management with semantic search, summarization, and Q&A capabilities powered by OpenAI, Mistral AI, and Anthropic Claude.

🎯 Features

🔧 MCP Tools Available

📄 Document Ingestion: Upload and process documents (PDF, TXT, DOCX, images with OCR)
🔍 Semantic Search: Find relevant content using natural language queries
📝 Summarization: Generate summaries in different styles (concise, detailed, bullet points, executive)
🏷️ Tag Generation: Automatically generate relevant tags for content
❓ Q&A System: Ask questions about your documents using RAG (Retrieval-Augmented Generation)
📊 Categorization: Classify content into predefined or custom categories
🔄 Batch Processing: Process multiple documents at once
📈 Analytics: Get insights and statistics about your content

🚀 Powered By

🧠 OpenAI GPT models for powerful text generation and understanding
🔥 Mistral AI for efficient text processing and analysis
🤖 Anthropic Claude for advanced reasoning (available as a specific choice or fallback)
🔗 Sentence Transformers for semantic embeddings
📚 FAISS for fast similarity search
👁️ Tesseract OCR for image text extraction
🎨 Gradio for the user interface and MCP server functionality

LLM Strategy: The agent intelligently selects the best available LLM for most generative tasks when 'auto' model selection is used, prioritizing OpenAI, then Mistral, and finally Anthropic. Users can also specify a particular model family (e.g., 'gpt-', 'mistral-', 'claude-').

📋 Complete File Structure

intelligent-content-organizer/ ├── app.py # Main Gradio app and MCP server ├── config.py # Configuration management ├── mcp_server.py # mcp server tools ├── requirements.txt # Dependencies ├── README.md # Documentation ├── .gitignore # Git ignore rules ├── core/ # Core processing logic │ ├── init.py │ ├── models.py # Data models │ ├── document_parser.py # Document processing │ ├── text_preprocessor.py # Text cleaning and processing │ └── chunker.py # Text chunking strategies ├── services/ # Backend services │ ├── init.py │ ├── embedding_service.py # Sentence transformers integration │ ├── llm_service.py # Anthropic + Mistral integration │ ├── ocr_service.py # Mistral OCR integration │ ├── vector_store_service.py # FAISS vector storage │ └── document_store_service.py # Document metadata storage └── mcp_tools/ # MCP tool definitions ├── init.py ├── ingestion_tool.py # Document ingestion tool ├── search_tool.py # Semantic search tool ├── generative_tool.py # AI generation tool └── utils.py # Utility functions

🎯 Key Features Implemented

Full MCP Server: Complete implementation with all tools exposed
Multi-Modal Processing: PDF, TXT, DOCX, and image processing with OCR
Advanced Search: Semantic search with FAISS, filtering, and multi-query support
AI-Powered Features: Summarization, tagging, categorization, Q&A with RAG
Production Ready: Error handling, logging, caching, rate limiting
Gradio UI: Beautiful web interface for testing and direct use
Anthropic + Mistral: Dual LLM support with fallbacks

🎥 Demo Video

📹 Watch the demo video

The demo shows the MCP server in action, demonstrating document ingestion, semantic search, and Q&A capabilities, utilizing the configured LLM providers.

🛠️ Installation

Prerequisites

Python 3.9+
API keys for OpenAI and Mistral AI. An Anthropic API key.
MCP Tools Reference (Tool parameters like model allow specifying "auto" or a specific model family like "gpt-", "mistral-", "claude-")
ingest_document
- Process and index a document for searching.
- Parameters:
  - file_path (string): Path to the document file (e.g., an uploaded file path).
  - file_type (string, optional): File type/extension (e.g., ".pdf", ".txt"). If not provided, it's inferred from file_path.
- Returns:
  - success (boolean): Whether the operation succeeded.
  - document_id (string): Unique identifier for the processed document.
  - chunks_created (integer): Number of text chunks created.
  - message (string): Human-readable result message.
semantic_search
- Search through indexed content using natural language.
- Parameters:
  - query (string): Search query.
  - top_k (integer, optional): Number of results to return (default: 5).
  - filters (object, optional): Search filters (e.g., {"document_id": "some_id"}).
- Returns:
  - success (boolean): Whether the search succeeded.
  - results (array of objects): Array of search results, each with content and score.
  - total_results (integer): Number of results found.
summarize_content
- Generate a summary of provided content.
- Parameters:
  - content (string, optional): Text content to summarize.
  - document_id (string, optional): ID of document to summarize. (Either content or document_id must be provided).
  - style (string, optional): Summary style: "concise", "detailed", "bullet_points", "executive" (default: "concise").
  - model (string, optional): Specific LLM to use (e.g., "gpt-4o-mini", "mistral-large-latest", "auto"). Default: "auto".
- Returns:
  - success (boolean): Whether summarization succeeded.
  - summary (string): Generated summary.
  - original_length (integer): Character length of original content.
  - summary_length (integer): Character length of summary.
generate_tags
- Generate relevant tags for content.
- Parameters:
  - content (string, optional): Text content to tag.
  - document_id (string, optional): ID of document to tag. (Either content or document_id must be provided).
  - max_tags (integer, optional): Maximum number of tags (default: 5).
  - model (string, optional): Specific LLM to use. Default: "auto".
- Returns:
  - success (boolean): Whether tag generation succeeded.
  - tags (array of strings): Array of generated tags.
answer_question
- Answer questions using RAG over your indexed content.
- Parameters:
  - question (string): Question to answer.
  - context_filter (object, optional): Filters for context retrieval (e.g., {"document_id": "some_id"}).
  - model (string, optional): Specific LLM to use. Default: "auto".
- Returns:
  - success (boolean): Whether question answering succeeded.
  - answer (string): Generated answer.
  - sources (array of objects): Source document chunks used for context, each with document_id, chunk_id, and content.
  - confidence (string, optional): Confidence level in the answer (LLM-dependent, might not always be present).

📊 Performance

Embedding Generation: ~100-500ms per document chunk Search: <50ms for most queries Summarization: 1-5s depending on content length Memory Usage: ~200-500MB base + ~1MB per 1000 document chunks Supported File Types: PDF, TXT, DOCX, PNG, JPG, JPEG, BMP, TIFF