MCP-Upstage-Server
OfficialServer Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
| UPSTAGE_API_KEY | Yes | Your Upstage API key from Upstage Console (https://console.upstage.ai) |
Instructions
Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.
This server publishes no instructions, or was last inspected before Glama recorded them.
Capabilities
Features and capabilities supported by this server
Protocol revision2025-11-25
| Capability | Details |
|---|---|
| tools | {} |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| parse_documentC | Parse a document using Upstage AI's document digitization API. This tool extracts the structure and content from various document types, including PDFs, images, and Office files. It preserves the original formatting and layout while converting the document into a structured format. Supported file formats include: PDF, JPEG, PNG, TIFF, and other common document formats. |
| extract_informationA | Extract structured information from documents using Upstage Universal Information Extraction. This tool can extract key information from any document type without pre-training. You can either provide a schema defining what information to extract, or let the system automatically generate an appropriate schema based on the document content. Supported file formats: JPEG, PNG, BMP, PDF, TIFF, HEIC, DOCX, PPTX, XLSX Max file size: 50MB Max pages: 100 SCHEMA FORMAT: When auto_generate_schema is false, provide schema in this exact format: { "type": "json_schema", "json_schema": { "name": "document_schema", "schema": { "type": "object", "properties": { "field_name": { "type": "string|number|array|object", "description": "What to extract" } } } } } Example schema_json: {"type":"json_schema","json_schema":{"name":"document_schema","schema":{"type":"object","properties":{"company_name":{"type":"string","description":"Company name"},"invoice_number":{"type":"string","description":"Invoice number"},"total_amount":{"type":"number","description":"Total amount"}}}}} |
| generate_schemaA | Generate an extraction schema for a document using Upstage AI's schema generation API. This tool analyzes a document and automatically generates a JSON schema that defines the structure and fields that can be extracted from similar documents. The generated schema can then be used with the extract_information tool when auto_generate_schema is set to false. This is useful when you want to:
Supported file formats: JPEG, PNG, BMP, PDF, TIFF, HEIC, DOCX, PPTX, XLSX Max file size: 50MB Max pages: 100 The tool returns both a readable schema object and a schema_json string that can be directly copied and used with the extract_information tool. |
| classify_documentA | Classify a document into predefined categories using Upstage AI's document classification API. This tool analyzes a document and classifies it into one of several predefined categories such as invoice, receipt, contract, CV, bank statement, and others. You can use the default classification schema or provide your own custom classification categories. Supported file formats: JPEG, PNG, BMP, PDF, TIFF, HEIC, DOCX, PPTX, XLSX Max file size: 50MB Max pages: 100 DEFAULT CATEGORIES:
CUSTOM CATEGORIES: Simply provide an array of categories in schema_json: [ {"const": "category1", "description": "Description of category 1"}, {"const": "category2", "description": "Description of category 2"}, {"const": "others", "description": "Other"} ] Example custom schema_json: [{"const":"medical","description":"Medical records or health documents"},{"const":"legal","description":"Legal documents"},{"const":"financial","description":"Financial statements or reports"},{"const":"others","description":"Other"}] |
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
No prompts | |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
No resources | |
TDQS
Scored across 4 tools
The tools have mostly distinct purposes: classify_document categorizes documents, extract_information pulls structured data, generate_schema creates schemas for extraction, and parse_document digitizes content. However, extract_information and parse_document could be confused as both involve extracting content from documents, though extract_information focuses on structured data fields while parse_document preserves formatting and layout. The descriptions help clarify this overlap.
All tool names follow a consistent verb_noun pattern with snake_case: classify_document, extract_information, generate_schema, and parse_document. This uniformity makes the tool set predictable and easy to understand, with no deviations in naming style.
With 4 tools, the count is reasonable for a document processing server, covering classification, extraction, schema generation, and digitization. It's slightly lean but well-scoped, as each tool serves a distinct function in the document AI workflow, though some might expect additional tools like summarization or translation for completeness.
The tool set covers core document AI operations: classification, information extraction, schema generation, and digitization. Minor gaps exist, such as missing summarization, translation, or document editing tools, but agents can work around these with the provided tools. The surface supports key workflows like categorizing documents and extracting structured data effectively.