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suparse

Suparse MCP Server

Official
by suparse

Extract Folder

extract_folder

Extract structured data from all supported files in a local folder using Suparse. Returns task IDs for later download or direct JSON results.

Instructions

Process all supported files in an immediate local folder through Suparse. Defaults to result_mode defer, which uploads and polls only, then returns compact task_id/document_ids for later download_results. Use result_mode return_json only when you need full JSON extractions in the MCP response. cleanup is only valid with return_json.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
splitNoEnable auto-splitting of multi-page documents with mixed document types.
api_urlNoOptional API base URL. Defaults to SUPARSE_API_URL or Suparse production API.
cleanupNoOnly valid with result_mode return_json. Deletes processed Suparse documents after JSON results are returned, so later exports cannot be fetched from those document IDs.
folder_pathYesLocal folder containing supported document files.
result_modeNoControls whether extraction results in json format are returned directly. Use return_json only when you need the full JSON extraction in the MCP response. In all other cases you can retrieve the results in format of choice using download_resultsdefer
template_idNoOptional extraction template ID. Use only a non-system team template ID from list_templates. Do not pass system template IDs directly; ask the user to add the matching system template to their templates first. Omit to let Suparse auto-detect.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
totalYes
failedYes
succeededYes
result_modeYes
Behavior3/5

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

Discloses default result_mode and cleanup constraints, but lacks details on side effects like file deletion or non-idempotency. No annotations provided.

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

Conciseness5/5

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

Three sentences, front-loaded with main action, no redundant words.

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

Completeness4/5

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

Output schema present; description covers return values (task_id/document_ids vs JSON) and main complexity, though some parameters (split) lack behavioral context.

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

Parameters4/5

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

Schema covers all parameters 100%; description adds value for result_mode (defer vs return_json) and template_id (system template warning).

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

Purpose5/5

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

Clearly states it processes all supported files in a folder via Suparse, distinct from extract_file and other siblings.

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

Usage Guidelines4/5

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

Explains when to use defer vs return_json and cleanup validity, but doesn't explicitly exclude alternatives.

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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