MCP-Upstage-Server

MCP-Upstage-Server

Node.js/TypeScript implementation of the MCP server for Upstage AI services.

Features

• Document Parsing: Extract structure and content from various document types (PDF, images, Office files)
• Information Extraction: Extract structured information using custom or auto-generated schemas
• Schema Generation: Automatically generate extraction schemas from document analysis
• Document Classification: Classify documents into predefined categories (invoice, receipt, contract, etc.)
• Built with TypeScript for type safety
• Dual transport support: stdio (default) and HTTP Streamable
• Async/await pattern throughout
• Comprehensive error handling and retry logic
• Progress reporting support

Installation

Prerequisites

• Node.js 18.0.0 or higher
• Upstage API key from Upstage Console

Install from npm

# Install globally
npm install -g mcp-upstage-server

# Or use with npx (no installation required)
npx mcp-upstage-server

Install from source

# Clone the repository
git clone https://github.com/UpstageAI/mcp-upstage.git
cd mcp-upstage/mcp-upstage-node

# Install dependencies
npm install

# Build the project
npm run build

# Set up environment variables
cp .env.example .env
# Edit .env and add your UPSTAGE_API_KEY

Usage

Running the server

# With stdio transport (default)
UPSTAGE_API_KEY=your-api-key npx mcp-upstage-server

# With HTTP Streamable transport
UPSTAGE_API_KEY=your-api-key npx mcp-upstage-server --http

# With HTTP transport on custom port
UPSTAGE_API_KEY=your-api-key npx mcp-upstage-server --http --port 8080

# Show help
npx mcp-upstage-server --help

# Development mode (from source)
npm run dev

# Production mode (from source)
npm start

Integration with Claude Desktop

Option 1: stdio transport (default)

{
  "mcpServers": {
    "upstage": {
      "command": "npx",
      "args": ["mcp-upstage-server"],
      "env": {
        "UPSTAGE_API_KEY": "your-api-key-here"
      }
    }
  }
}

Option 2: HTTP Streamable transport

{
  "mcpServers": {
    "upstage-http": {
      "command": "npx",
      "args": ["mcp-upstage-server", "--http", "--port", "3000"],
      "env": {
        "UPSTAGE_API_KEY": "your-api-key-here"
      }
    }
  }
}

Transport Options

stdio Transport (Default)

• Pros: Simple setup, direct process communication
• Cons: Single client connection only
• Usage: Default mode, no additional configuration needed

HTTP Streamable Transport

• Pros: Multiple client support, network accessible, RESTful API
• Cons: Requires port management, network configuration
• Endpoints:
  • POST /mcp - Main MCP communication endpoint
  • GET /mcp - Server-Sent Events stream
  • GET /health - Health check endpoint

Available Tools

parse_document

Parse a document using Upstage AI's document digitization API.

Parameters:

• file_path (required): Path to the document file
• output_formats (optional): Array of output formats (e.g., ['html', 'text', 'markdown'])

Supported formats: PDF, JPEG, PNG, TIFF, BMP, GIF, WEBP

extract_information

Extract structured information from documents using Upstage Universal Information Extraction.

Parameters:

• file_path (required): Path to the document file
• schema_path (optional): Path to JSON schema file
• schema_json (optional): JSON schema as string
• auto_generate_schema (optional, default: true): Auto-generate schema if none provided

Supported formats: JPEG, PNG, BMP, PDF, TIFF, HEIC, DOCX, PPTX, XLSX

generate_schema

Generate an extraction schema for a document using Upstage AI's schema generation API.

Parameters:

• file_path (required): Path to the document file to analyze

Supported formats: JPEG, PNG, BMP, PDF, TIFF, HEIC, DOCX, PPTX, XLSX

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.

Use cases:

• Create reusable schemas for multiple similar documents
• Have more control over extraction fields
• Ensure consistent field naming across extractions

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_document

Classify a document into predefined categories using Upstage AI's document classification API.

Parameters:

• file_path (required): Path to the document file to classify
• schema_path (optional): Path to JSON file containing custom classification schema
• schema_json (optional): JSON string containing custom classification schema

Supported formats: JPEG, PNG, BMP, PDF, TIFF, HEIC, DOCX, PPTX, XLSX

This tool analyzes a document and classifies it into categories. By default, it uses a comprehensive set of document types, but you can provide custom classification categories.

Default categories:

• invoice, receipt, contract, cv, bank_statement, tax_document, insurance, business_card, letter, form, certificate, report, others

Use cases:

• Automatically sort and organize documents by type
• Filter documents for specific processing workflows
• Build document management systems with automatic categorization

Schema Guide for Information Extraction

When auto_generate_schema is false, you need to provide a custom schema. Here's how to format it correctly:

📋 Basic Schema Structure

The schema must follow this exact structure:

{
  "type": "json_schema",
  "json_schema": {
    "name": "document_schema",
    "schema": {
      "type": "object",
      "properties": {
        "field_name": {
          "type": "string|number|array|object",
          "description": "Description of what to extract"
        }
      }
    }
  }
}

❌ Common Mistakes

Wrong: Missing nested structure

{
  "company_name": {
    "type": "string"
  }
}

Wrong: Incorrect response_format

{
  "schema": {
    "company_name": "string"
  }
}

Wrong: Missing properties wrapper

{
  "type": "json_schema",
  "json_schema": {
    "name": "document_schema", 
    "schema": {
      "type": "object",
      "company_name": {
        "type": "string"
      }
    }
  }
}

✅ Correct Examples

Simple schema:

{
  "type": "json_schema",
  "json_schema": {
    "name": "document_schema",
    "schema": {
      "type": "object",
      "properties": {
        "company_name": {
          "type": "string",
          "description": "Name of the company"
        },
        "invoice_number": {
          "type": "string",
          "description": "Invoice number"
        },
        "total_amount": {
          "type": "number",
          "description": "Total invoice amount"
        }
      }
    }
  }
}

Complex schema with arrays and objects:

{
  "type": "json_schema",
  "json_schema": {
    "name": "document_schema",
    "schema": {
      "type": "object",
      "properties": {
        "company_info": {
          "type": "object",
          "properties": {
            "name": {"type": "string"},
            "address": {"type": "string"},
            "phone": {"type": "string"}
          },
          "description": "Company information"
        },
        "items": {
          "type": "array",
          "items": {
            "type": "object", 
            "properties": {
              "item_name": {"type": "string"},
              "quantity": {"type": "number"},
              "price": {"type": "number"}
            }
          },
          "description": "List of invoice items"
        },
        "invoice_date": {
          "type": "string",
          "description": "Invoice date in YYYY-MM-DD format"
        }
      }
    }
  }
}

🛠️ Schema Creation Helper

You can create schemas programmatically:

function createSchema(fields) {
  return JSON.stringify({
    "type": "json_schema",
    "json_schema": {
      "name": "document_schema",
      "schema": {
        "type": "object",
        "properties": fields
      }
    }
  });
}

// Usage example:
const schema = createSchema({
  "company_name": {
    "type": "string",
    "description": "Company name"
  },
  "total": {
    "type": "number", 
    "description": "Total amount"
  }
});

💡 Data Types

• "string": Text data (names, addresses, etc.)
• "number": Numeric data (amounts, quantities, etc.)
• "boolean": True/false values
• "array": Lists of items
• "object": Nested structures
• "null": Null values

📝 Best Practices

1. Always include descriptions: They help the AI understand what to extract
2. Use specific field names: invoice_date instead of date
3. Nest related fields: Group related information in objects
4. Validate your JSON: Use a JSON validator before using the schema
5. Test with simple schemas first: Start with basic fields before adding complexity

Classification Schema Guide

The classify_document tool uses a different schema format optimized for classification tasks. Here's how to create custom classification schemas:

📋 Simple Classification Categories

For custom categories, just provide an array of category objects:

[
  {"const": "category1", "description": "Description of category 1"},
  {"const": "category2", "description": "Description of category 2"},
  {"const": "others", "description": "Fallback category"}
]

The tool automatically wraps this in the proper schema structure for the API.

✅ Correct Classification Examples

Medical document classifier:

[
  {"const": "prescription", "description": "Medical prescription document"},
  {"const": "lab_result", "description": "Laboratory test results"},
  {"const": "medical_recor

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*README truncated. [View full README on GitHub](https://github.com/UpstageAI/mcp-upstage-server).*