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')))"

LandingAI ADE MCP Server

LandingAI ADE MCP Server

Features

Installation

Prerequisites

Option 1: Using uv (Recommended - Simplest)

Install uv (if not already installed)

Set up the project

Option 2: Using pip with Virtual Environment

Configuration

Set Your API Key

Claude Desktop Configuration

Configuration File Location

Configuration Examples

Using uv (Recommended)

Using Virtual Environment

After Configuration

Available Tools

`parse_document`

`extract_data`

`create_parse_job`

`get_parse_job_status`

`list_parse_jobs`

`process_folder`

`health_check`

File Size Guidelines

Error Handling

Common Error Codes

Troubleshooting

Common Issues and Solutions

"Could not connect to MCP server"

"Server disconnected"

"ModuleNotFoundError: No module named 'fastmcp'"

Platform-Specific Issues

Debug Steps

API Documentation

Related Resources

New MCP Servers

MCP directory API