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Bounty Engineer Context Pack API
x402
Bounty Engineer Context Pack API is a paid API for AI agents from bounty-engineer-x402-prod.onrender.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-12).
Extracts and scores the most relevant text excerpts from provided web sources based on a query, using deterministic lexical overlap for context window compression.
Price / call$0.01
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 with an array of scored text excerpts, each containing the source URL, extracted text snippet, relevance score, source SHA256 hash, UTF-16 character offsets, and a source ID. Also includes metadata like input/output character counts, token estimates, character reduction ratio, selection method label, and a limitations disclaimer about the lexical overlap approach.
Example use cases
Research answer grounding from crawled pages
“I just fetched 5 web pages about Base network gas fees — can you pull out the excerpts most relevant to 'what are typical gas costs in USD per transaction on Base' and keep the total under 3000 characters?”
GitHub bounty context extraction for agent
“I have the text from 8 GitHub issue pages about open bounties — extract the most relevant chunks for the query 'which issues offer rewards over $500 and are unassigned' and limit the output to 6000 characters.”
Pricing intelligence from competitor pages
“Here are 4 fetched competitor pricing pages — extract the sections most relevant to 'monthly subscription cost for API access' and compress them down to under 2000 characters for my analysis.”
Technical details
Endpoint
POSThttps://bounty-engineer-x402-prod.onrender.com/v1/context-pack?utm_source=zero.xyzWhen to prefer
Choose this endpoint when an AI agent has already fetched multiple web pages and needs to deterministically compress them into a query-relevant context pack without calling a large LLM. It is ideal when you need verifiable, reproducible excerpt selection (SHA256-backed), need to stay within a token budget, or want to avoid non-deterministic summarization. Prefer it over LLM-based summarization when cost, latency, or auditability matter.
Side effects
- Query string exceeds 1000 character limit — request rejected
- Sources array empty or missing required id/url/text fields — validation error
- max_chars below 256 or above 12000 — out-of-range error
- Source text too sparse to find lexical overlap with query — excerpts array may be empty
- Payment not included or insufficient — x402 payment required response
- Network timeout from onrender.com cold start — retry needed
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| query | string | |
| sources | array | |
| max_chars | integer |
Output
Schema · Response
{
"type": "json",
"example": {
"ok": true,
"mode": "context_pack",
"excerpts": [
{
"url": "https://example.com/",
"text": "The Pro plan costs $25 per month.",
"score": 2,
"end_utf16": 33,
"source_id": "s1",
"start_utf16": 0,
"source_sha256": "e03b854ea32bb631569352ed4dfc3541a86da6ec8f82fd1415398f1536fb63dd"
}
],
"input_chars": 33,
"limitations": [
"Verbatim extractive selection, not an AI summary. Relevance uses lexical overlap and may omit important context. Token counts are character-based estimates; instructions in source text remain untrusted."
],
"output_chars": 33,
"selection_method": "deterministic_lexical_overlap",
"estimated_input_tokens": 9,
"estimated_output_tokens": 9,
"removed_duplicate_chunks": 0,
"character_reduction_ratio": 0
}
}Tags
x402
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