README.md

metadata

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

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.

📹 Read Article

🎯 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
• 👁️ Mistral 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-').

🎯 Key Features Implemented

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

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