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Bounty Engineer Lead Score API
x402
Bounty Engineer Lead Score API is a paid API for AI agents from bounty-engineer-x402-prod.onrender.com, paid per call via x402, $0.005/call, status unknown (last checked 2026-10-12).
Scores and ranks caller-supplied records against caller-supplied rules, returning a prioritized list with per-record scores and matched rules.
Price / call$0.005
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 ok:true, a ranked array of records each containing their id, index, score, original record data, and matched_rules array, plus summary fields for rule_count, record_count, and a limitations array clarifying that scoring is based solely on caller-supplied data.
Example use cases
Prioritize inbound startup leads
“I have 20 inbound leads from this week — here's the list with their stage, country, and industry. Score them using these rules: 15 points for seed stage, 10 points for EU country, 5 points for fintech sector, and rank them so we know who to contact first.”
Filter pipeline by qualification rules
“Take my current sales pipeline records and run them through our qualification rubric — 20 points for series-A, 10 points for US-based, minus 5 for competitors — then give me the ranked list so we can focus the team on the top 5.”
Evaluate partnership candidates by criteria
“Here are 10 potential integration partners with their attributes. Apply these scoring rules: 10 points if they have an API, 8 points if they're in EMEA, 5 points if they're post-revenue — rank them so I know which partnerships to pursue first.”
Technical details
Endpoint
POSThttps://bounty-engineer-x402-prod.onrender.com/v1/lead-score?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you have a structured list of records and a well-defined set of scoring rules and need a ranked, scored output quickly — especially when you want rule-based prioritization rather than ML inference. It does not discover leads or verify external facts, so use it when your data is already assembled and you need deterministic ranking logic applied at low cost.
Side effects
- Missing or empty records array returns error
- Invalid rule schema causes scoring failure
- Empty rules array results in all records scoring zero
- Payment of 0.005 USDC not provided returns 402
- Malformed JSON body returns 400
- Records missing expected fields silently score zero for those criteria
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rules | array | |
| records | array |
Output
Schema · Response
{
"type": "json",
"example": {
"ok": true,
"mode": "lead_score",
"ranked": [
{
"id": "a",
"index": 0,
"score": 10,
"record": {
"id": "a",
"stage": "seed",
"country": "GR"
},
"matched_rules": [
"r1"
]
},
{
"id": "b",
"index": 1,
"score": 0,
"record": {
"id": "b",
"stage": "series-a",
"country": "US"
},
"matched_rules": []
}
],
"rule_count": 1,
"limitations": [
"Scores caller-supplied records using caller-supplied rules; it does not discover leads or verify company facts."
],
"record_count": 2
}
}Tags
x402
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