Aparavi MCP Server
Integrates with Aparavi's document processing API to allow LLMs to process documents, extract clean text, and perform OCR on diagrams.
README
Aparavi MCP Server
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.
Features
- ๐ Document processing via Aparavi API
- ๐งน Clean text extraction without metadata
- ๐ MCP-compliant interface
- โ๏ธ Environment-based configuration
- ๐ Async processing support
- ๐ฆ Easy installation via NPX
- ๐ OCR capabilities for system diagrams
- ๐ Python-based with Node.js wrapper
Table of Contents
- Prerequisites
- Quick Start
- Installation
- Configuration
- Usage
- API Documentation
- Testing
- Project Structure
- Contributing
Prerequisites
- Python 3.8 or higher
- Node.js 14 or higher
- Git (for development setup)
Installation
For Users
There are two ways to install the MCP server as a user:
-
Get your API Key: For EU Users https://dtc.aparavi.eu/usage or US Users https://dtc.aparavi.com/usage
-
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 -
Add Server to your Client Update your
MCP_config.jsonfile in the client with this:{ "mcpServers": { "aparavi": { "serverUrl": "http://localhost:8000/mcp" } } }
For Developers
For local development and testing:
-
Clone the Repository
git clone https://github.com/AparaviSoftware/mcp-server cd mcp-server -
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" -
Set Up Python Environment
npx aparavi-mcp@latest -
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.pyTo test different tools or files, open
tests/test_tool.pyand modify themain()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)
Configuration
Required Environment Variables
APARAVI_API_KEY: Your Aparavi API key (required)APARAVI_API_URL: Your Aparavi API server (required)
Optional Environment Variables
VISION_API_KEY: Your Mistral Vision API key (required only for video processing tool)- Only needed if you want to use the
Aparavi_Video_Processortool - 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"
- Only needed if you want to use the
Project Structure
aparavi-mcp/
โโโ bin/ # Executable scripts
โ โโโ index.js # Node.js entry point
โ โโโ setup.sh # Python environment setup
|__ prompts/ #Preconfigured prompts
โโโ tools/ # MCP tool implementations
โโโ resources/ # Configuration and resources
โโโ tests/ # Test files
โโโ mcp-server.py # Main Python server
โโโ requirements.txt # Python dependencies
โโโ package.json # Node.js package config
License
This project is licensed under the MIT License - see the LICENSE file for details.
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