Sold by: Upstage
Extract structured information or convert messy documents into clean HTML - all with a single API. No fine-tuning, , no re-training, just results.
Overview
Upstage Document Intelligence - AI Agent is an all-in-one REST API solution that lets you unlock the value of unstructured documents instantly. It includes two capabilities:
Document Parse
Transform unstructured PDFs, scanned files, or multi-layout reports into clean, layout-preserving HTML or Markdown - perfect for LLM pipelines, RAG indexing, summarization, or search.
- LLM-Ready Output: Converts scanned or digital PDFs into richly structured HTML, preserving tables, charts, layout, and visual context.
- Layout-Aware Parsing: Handles rotated pages, checkbox states, multi-page tables, and embedded visuals.
- Blazing Fast: Parses 100 pages in under 1 minute - up to 10x faster than alternatives.
- High Accuracy: Benchmarked with industry-leading performance on DP-Bench (TEDS score 93+).
Information Extract
Extract key data from documents as structured JSON - aligned to your custom or auto-generated schema. Ideal for automating workflows.
- Universal Document Compatibility: Extracts information from any document format, including complex PDFs, images, and Office documents.
- Schema-Agnostic Flexibility: Instantly adapts to different schemas, providing structured outputs without additional customization.
- Hidden and Implied Data Extraction: Surfaces not only visible text but also inferred values and line-item calculations.
- No Fine-Tuning Required: Works out-of-the-box without template creation or model adjustments.
- Efficient JSON Conversion: Converts extracted information directly into structured JSON key-value pairs for seamless integration.
Whether you're building an RAG system or automating business processes, Document Intelligence makes document understanding radically simple.
Typical use cases:
- Contract analysis
- Invoice & receipt extraction
- Loan & insurance form processing
- Academic paper summarization
- Document chunking for LLM ingestion
Highlights
- Two-in-One Intelligence - Choose between schema-aligned JSON(information extract) or HTML/Markdown output(document parse) depending on your use case.
- Zero Training Required - Works out-of-the-box with messy scans, complex tables, and diverse document layouts.
- Enterprise-Scale Performance - Sync up to 100 pages or async up to 1000 pages, with blazing speed and audited accuracy.
Details
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Delivery method
Integration protocol
Type
Deployed on AWS
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Features and programs
Financing for AWS Marketplace purchases
AWS Marketplace now accepts line of credit payments through the PNC Vendor Finance program. This program is available to select AWS customers in the US, excluding NV, NC, ND, TN, & VT.
Pricing
Pricing is based on actual usage, with charges varying according to how much you consume. Subscriptions have no end date and may be canceled any time.
Additional AWS infrastructure costs may apply. Use the AWS Pricing Calculator to estimate your infrastructure costs.
Dimension | Description | Cost/unit |
|---|---|---|
InformationExtract__InferenceCount | Charged per page for each information-extract model inference. | $0.048 |
DocumentParse__InferenceCount | Charged per page for each document-parse model inference. | $0.012 |
Vendor refund policy
we currently do not support refund, but you can ask through support channel (https://get.support.upstage.ai/servicedesk/customer/portals )
Legal
Vendor terms and conditions
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Content disclaimer
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Delivery details
API-Based Agents & Tools
API-Based Agents and Tools integrate through standard web protocols. Your applications can make API calls to access agent capabilities and receive responses.
Additional details
Usage instructions
Upstage MCP
A Model Context Protocol (MCP) server for Upstage AI's document digitization and information extraction capabilities
Overview
The Upstage MCP Server provides a robust bridge between AI assistants and Upstage AI’s powerful document processing APIs. This server enables AI models—such as Claude—to effortlessly extract and structure content from various document types including PDFs, images, and Office files. The package supports multiple formats and comes with seamless integration options for Claude Desktop.
Key Features
- Document Digitization: Extract structured content from documents while preserving layout.
- Information Extraction: Retrieve specific data points using intelligent, customizable schemas.
- Multi-format Support: Handles JPEG, PNG, BMP, PDF, TIFF, HEIC, DOCX, PPTX, and XLSX.
- Claude Desktop Integration: Effortlessly connect with Claude and other MCP clients.
Prerequisites
Before using this server, ensure you have the following:
- Upstage API Key: Obtain your API key from Upstage Console
- Python 3.10+: The server requires Python version 3.10 or higher.
- The MCP server relies upon Astral UV to run
Installation & Configuration
This guide provides step-by-step instructions to set up and configure the mcp-upstage
Using uv (Recommended)
No additional installation is required when using uvx as it handles execution. However, if you prefer to install the package directly:
uv pip install mcp-upstageConfigure Claude Desktop
For integration with Claude Desktop, add the following content to your claude_desktop_config.json:
Using uvx Command (Recommended)
After adding the configuration, restart Claude Desktop to apply the changes.
Tip for macOS/Linux users: If connection issues occur, using the full path to your uv executable can improve reliability.
After configuring, restart Claude Desktop.
Available Tools
The server exposes two primary tools for AI models:
-
Document Parsing (parse_document):
- Description: Processes documents and extracts content while preserving structure.
- Parameter:
file_path – the path to the document to be processed. - Example Query:
"Can you parse the document at C:\Users\username\Documents\contract.pdf and provide a summary?"
-
Information Extraction (extract_information):
- Description: Extracts structured information from documents based on predefined or auto-generated schemas.
- Parameters:
file_path – the document file path;
schema_path (optional) – a JSON file with an extraction schema;
auto_generate_schema (default true) – whether to auto-generate a schema. - Example Query:
"Extract the invoice number, date, and total from C:\Users\username\Documents\invoice.pdf."
You can find detailed instructions:
We recommend to install @modelcontextprotocol/server-filesystem package to query document in your local file system.
Support
Vendor support
If you encounter any issues or have questions while using it, please contact us
AWS infrastructure support
AWS Support is a one-on-one, fast-response support channel that is staffed 24x7x365 with experienced and technical support engineers. The service helps customers of all sizes and technical abilities to successfully utilize the products and features provided by Amazon Web Services.
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