Upstage MCP Server

The Upstage MCP Server bridges the gap between AI assistants and Upstage AI’s advanced document processing APIs. It enables models such as Claude to ingest, digitize, and extract structured information from a wide array of documents—PDFs, images, and Office files—without the need for custom integration code. By exposing a Model Context Protocol interface, the server lets developers treat Upstage’s capabilities as first‑class tools in their AI workflows.

At its core, the server performs two complementary tasks. First, document digitization captures the textual and visual content of a file while preserving its layout. This is essential for downstream tasks that rely on positional context, such as converting a scanned invoice into a machine‑readable table. Second, information extraction applies user‑defined schemas to pull out specific data points—like dates, amounts, or identifiers—from the digitized content. The schema system is flexible enough to accommodate simple key/value pairs as well as nested structures, making it suitable for complex documents such as contracts or technical manuals.

Key capabilities include:

• Multi‑format support: JPEG, PNG, BMP, PDF, TIFF, HEIC, DOCX, PPTX, and XLSX are all natively handled.
• Seamless Claude Desktop integration: By configuring a single MCP server entry, developers can invoke document processing directly from the Claude UI.
• Persisted outputs: Results are stored in a predictable directory structure (), simplifying post‑processing and debugging.
• Custom schemas: Users can generate and store extraction schemas locally, enabling repeatable workflows across projects.

Typical use cases span from automating invoice reconciliation—where a PDF receipt is parsed and key financial fields are extracted—to legal document review, where clause positions and references need to be identified. In a data‑engineering pipeline, the server can act as an upstream ingestion step that normalizes heterogeneous documents before feeding them into a knowledge graph or search index.

Developers benefit from the server’s lightweight Python implementation and its reliance on Astral UV for fast, reproducible execution. The MCP interface abstracts away authentication and request formatting; once the is supplied, any MCP‑compliant client can call endpoints like or . This decoupling allows teams to experiment with different AI assistants or orchestrate complex multi‑step workflows—e.g., a Claude model asking the server to digitize a PDF, then another model refining the extracted data—without changing underlying integration logic.

In summary, the Upstage MCP Server delivers a turnkey solution for turning unstructured documents into structured data within AI‑driven applications, offering robust format support, schema flexibility, and effortless integration with popular MCP clients such as Claude Desktop.