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suparse

Suparse MCP Server

Official
by suparse

Extract File

extract_file

Upload a local document, extract structured data to JSON, CSV, XLSX, or Google Sheets. Supports auto-splitting and templates.

Instructions

Process one local document 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 the full JSON extraction 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.
file_pathYesLocal path to a supported document file.
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
Behavior4/5

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

With no annotations, the description discloses default mode (defer), behavior (uploads, polls, returns task_id/document_ids), and cleanup side effect (deletes documents). It does not mention rate limits, error handling, or idempotency, but covers the main behavioral traits adequately.

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?

Two sentences with no wasted words. Front-loaded with the primary purpose, then conditional details. Efficient and easy to parse.

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?

Given 6 parameters and an output schema, the description covers the key behaviors and parameter interactions. It could mention the output schema briefly, but the context is sufficient for an agent to use the tool correctly.

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 coverage is 100%, and the description adds value by explaining defaults, mode conditions, cleanup validity, and template_id usage guidance (avoiding system template IDs). This goes beyond the schema descriptions.

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 one local document through Suparse, with default behavior and mode options. Distinguishes from siblings like extract_folder and list_templates by specifying 'one local document' and mentioning related tools for retrieval.

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?

Explicitly describes when to use defer vs return_json, and that cleanup is only valid with return_json. Also references download_results for other formats. However, it does not explicitly exclude use cases or mention when not to use this tool.

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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