mcp_file_processor_gradio/README.md

metadata

title: MCP File Processor Tool
emoji: 📁
colorFrom: yellow
colorTo: red
sdk: gradio
sdk_version: 4.44.1
app_file: app.py
pinned: false
license: mit
tags:
  - mcp-server-track
  - mcp
  - file-processing
  - document-analysis
  - data-extraction
  - hackathon
  - gradio
python_version: 3.11.8

📁 MCP File Processor Tool

An AI-powered file processing tool that provides both a user-friendly Gradio interface and an MCP (Model Context Protocol) server endpoint for integration with AI assistants and other applications.

✨ Features

• 📄 Multi-Format Support: Process CSV, JSON, TXT, and other document formats
• 🔍 Smart Analysis: Extract content, structure, and metadata from uploaded files
• 📊 Data Insights: Provide statistical analysis and content summaries
• 🔌 MCP Server: Compliant with Model Context Protocol for AI assistant integration
• 🎨 Interactive UI: Modern Gradio interface with drag-and-drop file upload
• ⚡ Fast Processing: Efficient file parsing and analysis capabilities

🚀 Quick Start

Using the Web Interface

1. Visit this Space and interact with the web interface directly
2. Upload your file using the drag-and-drop interface
3. View analysis results including content extraction and insights
4. Download processed data in various formats

Supported File Types

• 📊 CSV Files: Data analysis, column profiling, statistical summaries
• 📋 JSON Files: Structure analysis, key exploration, validation
• 📄 Text Files: Content extraction, word counts, encoding detection
• 📑 Documents: Basic text extraction and metadata analysis

🔌 MCP Server Integration

This tool implements the Model Context Protocol (MCP) for integration with AI assistants, allowing programmatic file processing capabilities.

MCP Endpoint Details

• Endpoint URL: https://[this-space-url]/gradio_api/mcp/sse
• HTTP Method: POST
• Content-Type: application/json

Request Format

Send a POST request with the following JSON payload:

{
  "data": [
    "<file_content_or_path>",
    "<processing_type>"
  ]
}

Parameters:

• data[0] (string): File content or file path to process
• data[1] (string): Processing type ("analyze", "extract", "summarize")

Response Format

Successful responses return:

{
  "data": [
    {
      "status": "success",
      "file_type": "csv",
      "analysis": {
        "rows": 1000,
        "columns": 5,
        "summary": "Data analysis results..."
      },
      "content": "Processed content or insights..."
    }
  ]
}

🎯 Use Cases for AI Assistants

When integrated with AI assistants via MCP, this tool enables:

1. Data Analysis: "Analyze this CSV file and provide insights"
2. Content Extraction: "Extract text content from this document"
3. File Validation: "Check if this JSON file is properly formatted"
4. Data Summary: "Summarize the key information in this file"
5. Batch Processing: "Process multiple files and compare results"

🛠️ Technical Details

Processing Capabilities

• CSV Analysis: Column types, statistics, missing values, correlations
• JSON Validation: Structure validation, key analysis, nested object exploration
• Text Processing: Encoding detection, word count, language detection
• Metadata Extraction: File size, creation date, format details

API Configuration

• Timeout: 60 seconds for large file processing
• Size Limits: 50MB maximum file size
• Rate Limiting: Standard Gradio Space limits
• Error Handling: Comprehensive error messages and recovery

🏆 Hackathon Submission

This tool is submitted for the MCP Server Track of the hackathon, demonstrating:

• ✅ MCP Protocol Compliance: Full implementation of MCP server specification
• ✅ Production Ready: Robust error handling and file validation
• ✅ User Experience: Intuitive file upload interface with previews
• ✅ Documentation: Comprehensive API documentation and examples
• ✅ Integration Ready: Easy to integrate with AI assistants and workflows

🔧 Local Development

Prerequisites

• Python 3.11+
• Required packages for file processing (pandas, json, csv)

Installation

# Clone this repository
git clone [repository-url]
cd mcp_file_processor_gradio

# Install dependencies
pip install -r requirements.txt

# Run the application
python app.py

Testing MCP Endpoint Locally

# Test with curl
curl -X POST http://localhost:7860/gradio_api/mcp/sse \
  -H "Content-Type: application/json" \
  -d '{"data": ["sample_data.csv", "analyze"]}'

📊 Performance & Limitations

Strengths

• Fast file processing and analysis
• Multi-format support
• Comprehensive error handling
• MCP protocol compliance

Limitations

• 50MB file size limit
• Processing time depends on file size
• Limited to common file formats
• Requires well-formatted input files

📝 License

MIT License - Feel free to use and modify for your projects.

🤝 Contributing

This is a hackathon submission, but feedback and suggestions are welcome!

🏷️ Tags

#mcp-server-track #file-processing #data-analysis #gradio #ai-assistant #model-context-protocol

---

Built with ❤️ for the MCP Hackathon