buildvsbuy-ai.vercel.app
BuildVsBuy.ai — a WebMCP decision canvas. Humans and browser-integrated agents co-manipulate one structured build-vs-buy model (Auth or AI Web Scraping) via typed tools, not DOM scraping.
First seen 2026-11-05 · Last seen 2026-11-05 · Source: webmcp.com
Answer 1 · Act 8 · Transact 0 · Score: Not yet scored
Tool inspector
apply_human_preference_override
Record a human strategic override when math and judgment diverge (Act 3 / Prompt 4). For "own it" / core IP / crawl-is-core: ALWAYS pass pin_recommendation=true and override_reason stating ownership — this pins Build (sets is_core_ip), surfaces score gap vs math leader, and fills the Liability Ledger. Does NOT recalculate ranking and never silently swaps the math winner. Missing is_core_ip is OK — the tool sets it when pinning Build.
Input schema
| Property | Type | Required | Description |
|---|---|---|---|
override_reason |
string | yes | Human justification for overriding the math recommendation. |
heavily_favored_criterion |
string | no | Criterion the human weighted most in judgment. |
tolerance_level |
string | no | Tolerance for maintenance/security liabilities. |
pin_recommendation |
boolean | no | Pin recommendation to strategic choice (Build when is_core_ip). |
Raw JSON schema
{
"type": "object",
"properties": {
"override_reason": {
"type": "string",
"description": "Human justification for overriding the math recommendation."
},
"heavily_favored_criterion": {
"type": "string",
"enum": [
"time_to_prototype",
"cash_tco",
"maintenance_overhead",
"customization",
"security_risk",
"strategic_learning",
"vendor_lockin"
],
"description": "Criterion the human weighted most in judgment."
},
"tolerance_level": {
"type": "string",
"enum": [
"low",
"medium",
"high"
],
"description": "Tolerance for maintenance/security liabilities."
},
"pin_recommendation": {
"type": "boolean",
"description": "Pin recommendation to strategic choice (Build when is_core_ip)."
}
},
"required": [
"override_reason"
],
"additionalProperties": false
}
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