README.md

metadata

title: MissionControlMCP - Enterprise Automation Tools
emoji: 🚀
colorFrom: blue
colorTo: purple
sdk: gradio
sdk_version: 5.48.0
app_file: app.py
pinned: false
tags:
  - building-mcp-track-enterprise
  - mcp-in-action-track-enterprise
  - mcp
  - anthropic
  - enterprise-automation
  - gradio-hackathon
  - ai-agents
  - mcp-server

🚀 MissionControlMCP

Enterprise Automation MCP Server for Document Analysis, Data Processing & Business Intelligence

A fully functional Model Context Protocol (MCP) server providing 8 powerful enterprise automation tools for document processing, web scraping, semantic search, data visualization, and business analytics.

Built for the MCP 1st Birthday Hackathon – Winter 2025 (Tracks: Building MCP + MCP in Action - Enterprise).

🏆 Hackathon Submission | 🔧 Both Tracks | 🏢 Enterprise Category

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📱 Social Media & Links

• 🎬 Demo Video: Watch on YouTube ⭐ NEW
• 🔗 LinkedIn Post: View Announcement
• 🚀 Live Demo: Try on Hugging Face
• 💻 GitHub Repository: Source Code

---

📋 Table of Contents

• Overview
• Features
• Tools
• Installation
• Usage
• Tool Examples
• Claude Desktop Integration
• Development
• Testing
• Architecture
• Hackathon Submission

---

🎯 Overview

MissionControlMCP is an enterprise-grade MCP server that provides intelligent automation capabilities through 8 specialized tools. It enables AI assistants like Claude to perform complex document processing, data analysis, web research, and business intelligence tasks.

Key Capabilities

• 📄 Document Processing: Extract text from PDFs, process and summarize content
• 🌐 Web Intelligence: Fetch and parse web content with clean text extraction
• 🔍 Semantic Search: RAG-based vector search using FAISS and sentence transformers
• 📊 Data Visualization: Generate charts from CSV/JSON data
• 🔄 File Conversion: Convert between PDF, TXT, and CSV formats
• 📧 Email Classification: Classify email intents using NLP
• 📈 KPI Generation: Calculate business metrics and generate insights

---

🧪 Quick Test

# Test all tools with sample files
python demo.py

See TESTING.md for complete testing guide with examples!

---

✨ Features

• ✅ 8 Production-Ready Tools for enterprise automation
• ✅ MCP Compliant - Works with Claude Desktop and any MCP client
• ✅ Type-Safe - Built with Python 3.11+ and type hints
• ✅ Modular Architecture - Clean separation of concerns
• ✅ Comprehensive Testing - Test suite included
• ✅ Well Documented - Clear schemas and examples
• ✅ Vector Search - RAG implementation with FAISS
• ✅ Data Visualization - Base64 encoded chart generation
• ✅ NLP Classification - Rule-based intent detection

---

🛠️ Tools

1. pdf_reader

Extract text and metadata from PDF files.

Input:

• file_path: Path to PDF file

Output:

• Extracted text from all pages
• Page count
• Document metadata (author, title, dates)

---

2. text_extractor

Process and extract information from text.

Input:

• text: Raw text to process
• operation: 'clean', 'summarize', 'chunk', or 'keywords'
• max_length: Max length for summaries (default: 500)

Output:

• Processed text
• Word count
• Operation metadata

---

3. web_fetcher

Fetch and extract content from web URLs.

Input:

• url: URL to fetch
• extract_text_only: Extract text only (default: true)

Output:

• Clean text content or HTML
• HTTP status code
• Response metadata

---

4. rag_search

Semantic search using RAG (Retrieval Augmented Generation).

Input:

• query: Search query
• documents: List of documents to search
• top_k: Number of results (default: 3)

Output:

• Ranked search results with similarity scores
• Document snippets
• Relevance rankings

---

5. data_visualizer

Create data visualizations and charts.

Input:

• data: JSON or CSV string data
• chart_type: 'bar', 'line', 'pie', or 'scatter'
• x_column, y_column: Column names
• title: Chart title

Output:

• Base64 encoded PNG image
• Chart dimensions
• Column information

---

6. file_converter

Convert files between formats.

Input:

• input_path: Path to input file
• output_format: 'txt', 'csv', or 'pdf'
• output_path: Optional output path

Output:

• Output file path
• Conversion status
• File size

Supported Conversions:

• PDF → TXT
• TXT → CSV
• CSV → TXT

---

7. email_intent_classifier

Classify email intent using NLP.

Input:

• email_text: Email content to classify

Output:

• Primary intent (inquiry, complaint, request, feedback, meeting, order, urgent, follow_up, thank_you, application)
• Confidence score
• Secondary intents

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8. kpi_generator

Generate business KPIs and insights.

Input:

• data: JSON string with business data
• metrics: List of metrics - 'revenue', 'growth', 'efficiency', 'customer', 'operational'

Output:

• Calculated KPIs
• Executive summary
• Key trends and insights

---

📦 Installation

Prerequisites

• Python 3.11 or higher
• pip or uv package manager

Setup

1. Clone or download the repository:

cd mission_control_mcp

2. Install dependencies:

pip install -r requirements.txt

Or using uv:

uv pip install -r requirements.txt

Dependencies

• mcp - Model Context Protocol SDK
• pypdf2 - PDF processing
• requests + beautifulsoup4 - Web scraping
• pandas + numpy - Data processing
• faiss-cpu + sentence-transformers - Vector search
• matplotlib + seaborn - Data visualization
• scikit-learn + nltk - NLP and ML

---

🚀 Usage

Running the Server

For Development/Testing:

uvx mcp dev mission_control_mcp/mcp_server.py

Or with Python directly:

python mcp_server.py

For Production:

The server runs via stdio and is designed to be integrated with MCP clients like Claude Desktop.

---

💡 Tool Examples

Example 1: Text Extraction & Summarization

{
  "tool": "text_extractor",
  "arguments": {
    "text": "Your long document text here...",
    "operation": "summarize",
    "max_length": 200
  }
}

Example 2: Web Content Fetching

{
  "tool": "web_fetcher",
  "arguments": {
    "url": "https://example.com/article",
    "extract_text_only": true
  }
}

Example 3: Semantic Search

{
  "tool": "rag_search",
  "arguments": {
    "query": "machine learning algorithms",
    "documents": [
      "Document 1 about neural networks...",
      "Document 2 about decision trees...",
      "Document 3 about clustering..."
    ],
    "top_k": 3
  }
}

Example 4: Data Visualization

{
  "tool": "data_visualizer",
  "arguments": {
    "data": "{\"month\": [\"Jan\", \"Feb\", \"Mar\"], \"sales\": [1000, 1500, 1200]}",
    "chart_type": "bar",
    "x_column": "month",
    "y_column": "sales",
    "title": "Q1 Sales Report"
  }
}

Example 5: Email Intent Classification

{
  "tool": "email_intent_classifier",
  "arguments": {
    "email_text": "Hi, I need help with my recent order. It hasn't arrived yet and I'm wondering about the tracking status."
  }
}

Example 6: KPI Generation

{
  "tool": "kpi_generator",
  "arguments": {
    "data": "{\"revenue\": 1000000, \"costs\": 600000, \"customers\": 500, \"current_revenue\": 1000000, \"previous_revenue\": 800000}",
    "metrics": ["revenue", "growth", "efficiency"]
  }
}

---

🖥️ Claude Desktop Integration

Configuration

Add to your Claude Desktop config file (claude_desktop_config.json):

Windows: %APPDATA%\Claude\claude_desktop_config.json macOS: ~/Library/Application Support/Claude/claude_desktop_config.json

{
  "mcpServers": {
    "mission-control": {
      "command": "python",
      "args": [
        "C:/Users/YourUser/path/to/mission_control_mcp/mcp_server.py"
      ]
    }
  }
}

Or with uvx:

{
  "mcpServers": {
    "mission-control": {
      "command": "uvx",
      "args": [
        "mcp",
        "run",
        "C:/Users/YourUser/path/to/mission_control_mcp/mcp_server.py"
      ]
    }
  }
}

Usage in Claude

After configuration, restart Claude Desktop. You can then ask Claude to:

• "Extract text from this PDF file"
• "Fetch content from this website and summarize it"
• "Search these documents for information about X"
• "Create a bar chart from this sales data"
• "Classify the intent of this email"
• "Generate KPIs from this business data"

---

🧪 Testing

Run the comprehensive demo:

python demo.py

The demo includes:

• Text extraction and processing tests
• Web fetching tests
• RAG search demonstrations
• Data visualization generation
• Email classification examples
• KPI calculation tests
• Example JSON inputs for all tools

---

🏗️ Architecture

mission_control_mcp/
├── mcp_server.py              # Main MCP server
├── app.py                     # Gradio web interface
├── demo.py                    # Demo & test suite
├── requirements.txt           # Dependencies
├── README.md                  # Documentation
│
├── tools/                     # Tool implementations
│   ├── pdf_reader.py
│   ├── text_extractor.py
│   ├── web_fetcher.py
│   ├── rag_search.py
│   ├── data_visualizer.py
│   ├── file_converter.py
│   ├── email_intent_classifier.py
│   └── kpi_generator.py
│
├── models/                   # Data schemas
│   └── schemas.py
│
└── utils/                    # Utilities
    ├── helpers.py            # Helper functions
    └── rag_utils.py          # RAG/vector search utilities

Design Principles

• Modularity: Each tool is independently implemented
• Type Safety: Pydantic schemas for validation
• Error Handling: Comprehensive error catching and logging
• Clean Code: Well-documented with docstrings
• Testability: Easy to test individual components

---

🎖️ Hackathon Submission

Track 1: MCP Server

Server Name: MissionControlMCP

Description: Enterprise automation MCP server providing 8 specialized tools for document processing, web intelligence, semantic search, data visualization, and business analytics.

Key Features for Judges

1. Production-Ready: All 8 tools are fully implemented and tested
2. MCP Compliant: Follows MCP specification precisely
3. Real-World Value: Solves actual enterprise automation needs
4. Clean Architecture: Modular, maintainable, well-documented code
5. Advanced Features: RAG search with FAISS, data visualization, NLP classification
6. Comprehensive Testing: Full test suite with examples
7. Easy Integration: Works seamlessly with Claude Desktop

Technical Highlights

• Vector Search: FAISS-based semantic search with sentence transformers
• NLP Classification: Rule-based email intent classifier with confidence scoring
• Data Visualization: Dynamic chart generation with matplotlib
• File Processing: Multi-format support (PDF, TXT, CSV)
• Web Intelligence: Smart web scraping with clean text extraction
• Business Intelligence: KPI calculation with trend analysis

---

📝 Documentation & Examples

• EXAMPLES.md - Real-world use cases, workflows, and ROI examples
• TESTING.md - Complete testing guide with examples
• ARCHITECTURE.md - System design and architecture details
• API.md - Complete API documentation
• examples/ - Sample files for testing all tools:
  • sample_report.txt - Business report for text extraction
  • business_data.csv - Financial data for visualization & KPIs
  • sample_email_*.txt - Email samples for intent classification
  • sample_documents.txt - Documents for RAG search testing

---

�📝 License

This project is licensed under the MIT License - see the LICENSE file for details.

Created for the MCP 1st Birthday Hackathon – Winter 2025.

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🤝 Contributing

This project was built for the hackathon, but improvements and suggestions are welcome! Check out EXAMPLES.md for usage patterns and best practices.

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📧 Contact

For questions about this MCP server, please reach out through the hackathon channels.

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🌟 Acknowledgments

• Built with the Model Context Protocol SDK
• Powered by sentence-transformers, FAISS, and other open-source libraries
• Created for the MCP 1st Birthday Hackathon 2025

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Happy Automating! 🚀