ai.agenttrust/mcp
audit_task
Verify whether completed work meets a task specification using AI. Call get_fees() first to get the current fee amount and accepted assets. Each fee_hash is single-use (anti-replay protection). Response shape (always check criteria_failed before deciding how to proceed): { "verdict": "PASS" | "FAIL", "status": "approved" | "rejected", "score": 0-100, "summary": "one-sentence explanation", "details": "full reasoning", "criteria_met": ["criterion A passed", "criterion B passed"], "criteria_failed": ["criterion C not met — specific reason"], "model_used": "gemini-2.5-flash" } On PASS: proceed to payment or accept the deliverable. On FAIL: read criteria_failed to understand exactly what was missing. Each entry is a specific, actionable failure — not a generic rejection. Use them to tell the worker precisely what to fix before resubmitting.
Remote (network-hosted)
Other tools also called audit_task?
See providers with this name
Input Schema
{
"type": "object",
"properties": {
"task": {
"type": "string",
"title": "Task Specification",
"description": "The task requirements or specification the worker must meet."
},
"work": {
"type": "string",
"title": "Completed Work",
"description": "The work, output, or proof of completion to evaluate against the specification."
},
"fee_hash": {
"type": "string",
"title": "XRPL Payment Hash",
"description": "Transaction hash of the fee payment. For XRP: 64-char hex of an XRPL Payment tx. For USDC on Base: 0x-prefixed 66-char EVM tx hash. Each hash is single-use."
},
"task_category": {
"type": "string",
"default": "default",
"description": "Evaluation rubric. One of: default, creative, code, data, data_analysis, bug_bounty, legal, supply_chain."
},
"require_consensus": {
"type": "boolean",
"default": false,
"description": "When True, two AI models must independently agree before returning PASS. Recommended for high-stakes tasks."
}
}
}