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Bounty Engineer Text Chunker
x402
Bounty Engineer Text Chunker is a paid API for AI agents from bounty-engineer-x402-prod.onrender.com, paid per call via x402, $0.001/call, status unknown (last checked 2026-10-12).
Splits a text string into overlapping fixed-size chunks with SHA-256 hashes and UTF-16 byte offsets for downstream processing by AI agents.
Price / call$0.001
Success—
StatusUnknown
How this service works
Machine-buyable infrastructure for autonomous agents: API cost routing, margin optimization, funding-rate data, webpage extraction, result verification, spend controls, retry planning, state, tender search and GitHub bounty intelligence.
Output
Returns a JSON object containing an array of chunk objects, each with the chunk text, its sequential index, a SHA-256 hash, and UTF-16 start/end character offsets. Also returns metadata: total chunk count, input character count, chunk size used, overlap size used, and a truncated flag indicating whether the input was cut short.
Example use cases
RAG pipeline document ingestion
“Take this 5000-word research paper text and break it into 512-character chunks with 64 characters of overlap so I can store each piece in my vector database for semantic search.”
Webpage text preparation for embedding
“I scraped this webpage and got back a big block of text — can you chunk it into 128-character segments with 16-character overlap and give me the SHA-256 hash for each chunk so I can track deduplication?”
LLM context window segmentation
“This document is too long to fit in one prompt — please split it into 1024-character chunks with 100-character overlap so my agent can process each piece sequentially without losing context at the boundaries.”
Technical details
Endpoint
POSThttps://bounty-engineer-x402-prod.onrender.com/v1/text-chunker?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need deterministic, reproducible text chunking with per-chunk SHA-256 fingerprints and UTF-16 byte offsets — especially useful for RAG pipelines, vector database ingestion, or deduplication workflows. Prefer it over in-process chunking libraries when you need a consistent, auditable, payable micro-service call that integrates with the x402 agent commerce ecosystem.
Side effects
- Missing or empty 'text' field returns an error
- chunk_chars value of zero or negative may cause undefined behavior
- Very large text inputs may time out or be truncated (truncated: true)
- Non-integer values for chunk_chars or overlap_chars may be rejected
- overlap_chars equal to or greater than chunk_chars could cause infinite loop or error
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| text | string | |
| chunk_chars | integer | |
| overlap_chars | integer |
Output
Schema · Response
{
"type": "json",
"example": {
"ok": true,
"mode": "text_chunker",
"chunks": [
{
"text": "One two three four five six seven eight nine ten. One two three four five six seven eight nine ten. One two three four five six",
"index": 0,
"sha256": "32885d3ed2faf9ef13427870eaa245ec1db41662193b7e82c54184d5f6562c59",
"end_utf16": 127,
"start_utf16": 0
},
{
"text": "ee four five six seven eight nine ten.",
"index": 1,
"sha256": "cf7ed180f6a6e9beb991ae31adf3c8862553c393e7ae13745d7420cdabd337eb",
"end_utf16": 149,
"start_utf16": 111
}
],
"truncated": false,
"chunk_chars": 128,
"chunk_count": 2,
"input_chars": 149,
"overlap_chars": 16
}
}Tags
x402
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