A
licenseA
qualityC
maintenanceEnables AI assistants to extract and structure content from documents (PDFs, images, Office files) using Upstage AI's document digitization and information extraction APIs.
2
3
MIT
MCP-Upstage-Server
Officialby UpstageAI
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TDQS
A3.7/5.0
Scored across 4 tools
Disambiguation4/5
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.
Naming Consistency5/5
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.
Tool Count4/5
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.
Completeness4/5
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.
Maintenance
ActivityInactive
ResponsivenessNo issues