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Animica Document Structure Analyzer
x402
Animica Document Structure Analyzer is a paid API for AI agents from animica.dev, paid per call via x402, $0.002/call, status unknown (last checked 2026-09-13).
Analyzes raw document text and returns its structural breakdown — sections, headings, hierarchy, and layout organization.
Price / call$0.002
Success—
StatusUnknown
How this service works
Animica Python Cloud: deploy a Python function to animica.dev, get a public endpoint, and earn ANM every time someone runs it. Free AI (OpenAI-compatible, no key), free scheduled Workers, and an 80/20 developer split.
Output
A JSON object describing the structural breakdown of the submitted document text, including identified sections, headings, hierarchy levels, and organizational layout elements.
Example use cases
Research paper outline extraction
“I have a long research paper and I want to understand its structure before reading it — can you pull out all the sections and headings from this text: 'Abstract: We present... 1. Introduction... 2. Related Work... 3. Methodology...'”
Legal contract section mapping
“Can you break down the structure of this legal contract text so I can see all the clauses and sub-sections? Here's the full text: 'AGREEMENT FOR SERVICES\n1. Parties\n1.1 This agreement is between...'”
Document ingestion pipeline pre-processing
“Before I store this technical manual, I need to understand its document structure — what are the top-level sections, subsections, and how they're nested? The text starts with: 'Chapter 1: Installation\n1.1 Requirements\n1.2 Setup...'”
Technical details
Endpoint
POSThttps://animica.dev/x402/health/document/structureWhen to prefer
Choose this endpoint when you need to programmatically understand how a document is organized — its sections, headings, and hierarchy — without manually parsing it. It is ideal for pre-processing pipelines before storing, indexing, or summarizing documents, or when you need a structural outline quickly at $0.006/call with no API key or account required.
Side effects
- Empty or missing 'text' field returns an error or empty structure
- Extremely short text with no detectable structure may return a minimal or flat result
- Unformatted or poorly delimited text may produce low-confidence structural parsing
- Very large documents may hit payload limits or time out
- Malformed JSON request body returns a 400-level error
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| text | string | the document text |
Output
Schema · Response
{
"type": "json"
}Tags
x402
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