An MCP (Model Context Protocol) server that integrates with Aparavi's document processing capabilities. This server allows Language Models to process documents through Aparavi's API and receive cleaned text output.

• 📄 Document processing via Aparavi API
• 🧹 Clean text extraction without metadata
• 🔌 MCP-compliant interface
• ⚙️ Environment-based configuration
• 🚀 Async processing support
• 📦 Easy installation via NPX
• 🔍 OCR capabilities for system diagrams
• 🐍 Python-based with Node.js wrapper

• Prerequisites
• Quick Start
• Installation
  • For Users
  • For Developers
• Configuration
• Usage
  • Running as a User
  • Running for Development
• API Documentation
• Testing
• Project Structure
• Contributing

• Python 3.8 or higher
• Node.js 14 or higher
• Git (for development setup)

There are two ways to install the MCP server as a user:

1. Get your API Key: For EU Users https://dtc.aparavi.eu/usage or US Users https://dtc.aparavi.com/usage

2. Run the Server

   # Choose which Aparavi server you want to use and set API keys in terminal
   
    # For US users: 
    # Get Aparavi API Key from: https://dtc.aparavi.com/
    # Set APARAVI_API_URL to: https://eaas.aparavi.com
   
    # For EU users: 
    # Get Aparavi API Key from: https://dtc.aparavi.eu/
    # Set APARAVI_API_URL to: https://eaas.aparavi.eu
   
   # For Unix/Linux/macOS
   export APARAVI_API_KEY=your_api_key_here
   export APARAVI_API_URL=your_url_here
   
   # For Windows - Set API keys in Command Prompt
   set APARAVI_API_KEY=your_api_key_here
   set APARAVI_API_URL="your_url_here"
   
   # OR for Windows PowerShell
   $env:APARAVI_API_KEY="your_api_key_here"
   $env:APARAVI_API_URL="your_url_here"
   
   # Run the server (same command for all platforms)
   npx aparavi-mcp@latest

3. Add Server to your Client Update your MCP_config.json file in the client with this:

    {
      "mcpServers": {
        "aparavi": {
          "serverUrl": "http://localhost:8000/mcp"
        }
      }
    }
   

For local development and testing:

1. Clone the Repository

   git clone https://github.com/AparaviSoftware/mcp-server
   cd mcp-server

2. Set Environment Variables

   # For US users: https://eaas.aparavi.com
   # For EU users: https://eaas.aparavi.eu
   
   # For Unix/Linux/macOS
   export APARAVI_API_KEY=your_api_key_here
   export APARAVI_API_URL=your_url_here
   
   # For Windows - Set API keys in Command Prompt
   set APARAVI_API_KEY=your_api_key_here
   set APARAVI_API_URL="your_url_here"
   
   # OR for Windows PowerShell
   $env:APARAVI_API_KEY="your_api_key_here"
   $env:APARAVI_API_URL="your_url_here"

3. Set Up Python Environment

    npx aparavi-mcp@latest

4. Running Tests First, ensure your server is running (from step 1). Then you can run and configure tests:

   # Run the test tool
   python tests/test_tool.py

   To test different tools or files, open tests/test_tool.py and modify the main() function:

   def main():
       # Change the file path to test different documents
       file_path = "tests/testdata/test_document.txt"
       # Or try other test files:
       # file_path = "tests/testdata/SDD_RoadTrip.pdf"
       # file_path = "tests/testdata/system_diagram.jpeg"
   
       # Change the tool name to test different tools
       tool_name = "document_processor"
       # Available tools:
       # - "Aparavi_Document_Processor" (for text documents)
       # - "Advanced_OCR_Parser" (for diagrams/images)
   
       run_tool_test(file_path, tool_name)

• APARAVI_API_KEY: Your Aparavi API key (required)
• APARAVI_API_URL: Your Aparavi API server (required)

• VISION_API_KEY: Your Mistral Vision API key (required only for video processing tool)
  • Only needed if you want to use the Aparavi_Video_Processor tool
  • Get your API key from Mistral AI
  • Set it the same way as other environment variables:

    # Unix/Linux/macOS
    export VISION_API_KEY=your_mistral_api_key_here
    
    # Windows Command Prompt
    set VISION_API_KEY=your_mistral_api_key_here
    
    # Windows PowerShell
    $env:VISION_API_KEY="your_mistral_api_key_here"

aparavi-mcp/
├── bin/                    # Executable scripts
│   ├── index.js           # Node.js entry point
│   └── setup.sh           # Python environment setup
|__ prompts/               #Preconfigured prompts
├── tools/                 # MCP tool implementations
├── resources/             # Configuration and resources
├── tests/                 # Test files
├── mcp-server.py         # Main Python server
├── requirements.txt      # Python dependencies
└── package.json         # Node.js package config

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