How do I use LandingAI ADE MCP Server?

1. Click on "Install Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@LandingAI ADE MCP Server extract the invoice number and total amount from this PDF receipt" That's it! The server will respond to your query, and you can continue using it as needed. Here is a step-by-step guide with screenshots.

LandingAI ADE MCP Server

A Model Context Protocol (MCP) server providing direct integration with LandingAI's Agentic Document Extraction (ADE) API. Extract text, tables, and structured data from PDFs, images, and office documents.

Features

📄 Document Parsing - Parse entire documents and return markdown output
🔍 Data Extraction - Extract structured data using JSON schemas
⚡ Parse Jobs - Handle large documents with background processing
🛡️ Zero Data Retention - Privacy-focused processing support

Installation

Prerequisites

Python 3.9 or higher
LandingAI API key from LandingAI

Option 1: Using uv (Recommended - Simplest)

uv is a fast Python package manager that handles virtual environments automatically.

Install uv (if not already installed)

# macOS/Linux curl -LsSf https://astral.sh/uv/install.sh | sh # Or with Homebrew brew install uv

Set up the project

# Clone the repository git clone https://github.com/avaxia8/landingai-ade-mcp.git cd landingai-ade-mcp # Install dependencies with uv uv sync # Or if starting fresh: uv init uv add fastmcp httpx pydantic python-multipart aiofiles

Option 2: Using pip with Virtual Environment

# Clone the repository git clone https://github.com/avaxia8/landingai-ade-mcp.git cd landingai-ade-mcp # Create virtual environment python3 -m venv venv # Activate virtual environment # On macOS/Linux: source venv/bin/activate # On Windows: venv\Scripts\activate # Install dependencies pip install -r requirements.txt

Configuration

Set Your API Key

export LANDINGAI_API_KEY="your-api-key-here"

Claude Desktop Configuration

Configuration File Location

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

Configuration Examples

Using uv (Recommended)

{ "mcpServers": { "landingai-ade-mcp": { "command": "uv", "args": [ "--directory", "/path/to/landingai-ade-mcp", "run", "python", "-m", "server" ], "env": { "LANDINGAI_API_KEY": "your-api-key-here" } } } }

Using Virtual Environment

{ "mcpServers": { "landingai-ade-mcp": { "command": "/path/to/landingai-ade-mcp/venv/bin/python", "args": [ "/path/to/landingai-ade-mcp/server.py" ], "env": { "LANDINGAI_API_KEY": "your-api-key-here" } } } }

After Configuration

Save the configuration file
Restart Claude Desktop completely (quit and reopen)
The server should appear as "landingai-ade-mcp" in your MCP servers

Available Tools

`parse_document`

Parse entire documents and return markdown output.

# Parse a local file result = await parse_document( document_path="/path/to/document.pdf", model="dpt-2-latest", # optional split="page" # optional, for page-level splits ) # Parse from URL result = await parse_document( document_url="https://example.com/document.pdf" )

`extract_data`

Extract structured data from markdown using a JSON schema.

schema = { "type": "object", "properties": { "invoice_number": {"type": "string"}, "total": {"type": "number"} } } # Extract from markdown content string result = await extract_data( schema=schema, markdown="# Invoice\nInvoice #123\nTotal: $100.00" ) # Or extract from a markdown file result = await extract_data( schema=schema, markdown="/path/to/document.md" # Will detect if it's a file path ) # Or extract from URL result = await extract_data( schema=schema, markdown_url="https://example.com/document.md" )

`create_parse_job`

Create a parse job for large documents (>50MB recommended).

job = await create_parse_job( document_path="/path/to/large_document.pdf", split="page" # optional ) job_id = job["job_id"]

`get_parse_job_status`

Check status and retrieve results of a parse job.

status = await get_parse_job_status(job_id) # Check status if status["status"] == "completed": # For small files, data is included directly # For large files (>1MB), data is auto-fetched from output_url results = status["data"] elif status["status"] == "processing": print(f"Progress: {status['progress'] * 100:.1f}%")

`list_parse_jobs`

List all parse jobs with filtering and pagination.

jobs = await list_parse_jobs( page=0, # optional, default 0 pageSize=10, # optional, 1-100, default 10 status="completed" # optional filter )

`process_folder`

Process all supported files in a folder - parse documents or extract structured data.

Supported formats:

Images: APNG, BMP, DCX, DDS, DIB, GD, GIF, ICNS, JP2, JPEG, JPG, PCX, PNG, PPM, PSD, TGA, TIFF, WEBP
Documents: PDF, DOC, DOCX, PPT, PPTX, ODP, ODT

# Parse all PDFs in a folder result = await process_folder( folder_path="/path/to/documents", operation="parse", # or "extract" for structured data file_types="pdf", # optional filter model="dpt-2-latest" ) # Extract structured data from all documents schema = { "type": "object", "properties": { "invoice_number": {"type": "string"}, "total": {"type": "number"}, "date": {"type": "string"} } } result = await process_folder( folder_path="/path/to/invoices", operation="extract", schema=schema, file_types="pdf,jpg" # Process PDFs and images ) # Process everything with defaults result = await process_folder( folder_path="/path/to/mixed_documents" )

Features:

Automatic file size detection (uses direct parsing for <50MB, jobs for larger)
Concurrent processing with rate limiting
Progress tracking for long-running operations
Organized output in ade_results folder
Aggregated data for extraction operations
Continues processing even if individual files fail

`health_check`

Check server status and API connectivity.

health = await health_check() # Returns server status, API connectivity, available tools

File Size Guidelines

< 50MB: Use parse_document directly
> 50MB: Always use create_parse_job

Error Handling

result = await parse_document(document_path="/path/to/file.pdf") if result.get("status") == "error": print(f"Error: {result['error']}") print(f"Status Code: {result.get('status_code')}") else: # Process successful result markdown = result["markdown"]

Common Error Codes

401: Invalid API key
413: File too large (use parse jobs)
422: Validation error
429: Rate limit exceeded

Troubleshooting

Common Issues and Solutions

"Could not connect to MCP server"

Python not found: Make sure the Python path in your config is correct
# Find your Python path which python3
Module not found errors: Dependencies aren't installed in the Python environment
- If using uv: Run uv sync in the project directory
- If using venv: Activate it and run pip install -r requirements.txt
- Check that the Python path in config matches your environment
spawn python ENOENT: The system can't find Python
- Use the full path to Python (e.g., /usr/bin/python3 instead of just python)
- For virtual environments, use the full path to the venv's Python

"Server disconnected"

Check the server can run manually:
cd /path/to/landingai-ade-mcp python server.py # Should see: "Starting LandingAI ADE MCP Server"
Check API key is set:
echo $LANDINGAI_API_KEY
Check dependencies are installed:
python -c "import fastmcp, httpx, pydantic" # Should complete without errors

"ModuleNotFoundError: No module named 'fastmcp'"

This means fastmcp isn't installed in the Python environment being used:

If using virtual environment: The config is pointing to the wrong Python
Solution: Use uv or ensure the Python path matches your environment

Platform-Specific Issues

macOS: If you installed Python with Homebrew, the path might be /opt/homebrew/bin/python3 (Apple Silicon) or /usr/local/bin/python3 (Intel)

Windows: Use forward slashes in paths or escape backslashes: C:/path/to/python.exe or C:\\path\\to\\python.exe

Linux: Some systems use python3 instead of python. Always use python3 for clarity.

Debug Steps

Test the server standalone:
python server.py
Check MCP communication:
echo '{"jsonrpc": "2.0", "method": "initialize", "id": 1}' | python server.py
Verify configuration:
- Open Claude Desktop developer settings
- Check the logs for specific error messages
- Ensure all paths are absolute, not relative
Validate API key:
python -c "import os; print('API Key set:', bool(os.environ.get('LANDINGAI_API_KEY')))"