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PritamPatil2603

Upstage MCP Server

parse_document

Extract structured content and layout from PDFs, images, and Office documents using AI digitization. Preserves original formatting while converting documents into structured formats like HTML or markdown.

Instructions

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.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
file_pathYesPath to the document file to be processed
output_formatsNoOutput formats (e.g., 'html', 'text', 'markdown')
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, placing the full burden on the description. The description only mentions that the tool extracts content without discussing side effects, authentication, rate limits, or whether it modifies the document. This lack of behavioral disclosure is insufficient for a tool that processes files.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise at three sentences plus a format list. It front-loads the main purpose and avoids unnecessary detail. It could be slightly more structured, but overall it is efficient.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the complexity of document parsing, the description lacks information about the output structure, error handling, performance characteristics, and behavior with different input types. Without an output schema, the agent is left guessing what the tool returns. This is a significant gap.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema covers all parameters with 100% description coverage. The description adds value by enumerating supported file formats for file_path, but does not elaborate on output_formats (e.g., default behavior, accepted values). The complementary information is helpful but not extensive.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states that the tool parses documents using Upstage AI's API, extracting structure and content while preserving formatting. It lists supported file formats (PDF, JPEG, etc.). However, it does not differentiate itself from the sibling tool 'extract_information', which may cause confusion about when to use which.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides context about the API used and supported file types but does not specify when to use this tool versus the alternative 'extract_information'. It lacks explicit guidance on prerequisites, limitations, or recommended use cases.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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