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Glama
PritamPatil2603

Upstage MCP Server

by PritamPatil2603
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

Server Configuration

Describes the environment variables required to run the server.

NameRequiredDescriptionDefault
UPSTAGE_API_KEYYesYour Upstage API key for document processing.

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

CapabilityDetails
tools
{
  "listChanged": false
}
prompts
{
  "listChanged": false
}
resources
{
  "subscribe": false,
  "listChanged": false
}
experimental
{}

Tools

Functions exposed to the LLM to take actions

NameDescription
parse_documentB

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_informationC

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

Args:
    file_path: Path to the document file to process
    schema_path: Optional path to a JSON file containing the extraction schema
    schema_json: Optional JSON string containing the extraction schema
    auto_generate_schema: Whether to automatically generate a schema if none is provided

Prompts

Interactive templates invoked by user choice

NameDescription

No prompts

Resources

Contextual data attached and managed by the client

NameDescription

No resources

Deploy Server
One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

TDQS

A3.5/5.0

Scored across 2 tools

Disambiguation5/5

The two tools have clearly distinct purposes: parse_document extracts structure and layout, while extract_information extracts specific information. There is no overlap or ambiguity.

Naming Consistency5/5

Both tools follow a consistent verb_noun pattern (parse_document, extract_information), making it predictable and easy to understand.

Tool Count3/5

With only 2 tools, the server feels thin for the scope of document processing. While the tools are high-value, the count is borderline low.

Completeness4/5

The tools cover core digitization and information extraction, but lack operations like listing, deleting, or batch processing. These are minor gaps that agents can work around.

Maintenance

ActivityInactive
ResponsivenessNo issues

Resources