Evidence row 276864

What was observed

{
  "count": 8,
  "tools": [
    {
      "description": "Score whether a product listing is Agent-Ready for AI shopping agents (ACP/UCP era).\n\n    Platform-agnostic: works for Amazon, Shopify, Walmart, TikTok Shop or any storefront that\n    AI shopping assistants may read. Use it when a user asks whether their product will be\n    found, recommended, or auto-purchased by an AI agent.\n\n    Fully local, deterministic rule engine — no API key, no credits, no network call. Returns\n    four dimensions: structured attributes, entity clarity, trust & compliance, and agent\n    actionability, plus ranked fixes.\n\n    Args:\n        text: raw product copy — title plus bullets/description (required).\n        platform: amazon | shopify | walmart | tiktok | generic | auto (default auto-detected).\n        lang: en or zh for the report language (default en).",
      "inputSchema": {
        "properties": {
          "lang": {
            "default": "en",
            "title": "Lang",
            "type": "string"
          },
          "platform": {
            "default": "auto",
            "title": "Platform",
            "type": "string"
          },
          "text": {
            "title": "Text",
            "type": "string"
          }
        },
        "required": [
          "text"
        ],
        "title": "agent_ready_checkArguments",
        "type": "object"
      },
      "name": "agent_ready_check"
    },
    {
      "description": "Score how likely Amazon's AI (Rufus, COSMO) is to recommend a listing.\n\n    Free, deterministic, rule-based check on pasted listing copy (title, bullets, description).\n    Returns a compliance health score, an AI-readability score, and a combined AI Recommendation\n    Readiness Score (compliance * 0.55 + readability * 0.45) with actionable suggestions.\n\n    Use this as a fast baseline BEFORE generating or editing a listing. Do NOT use it for a full\n    compliance report - use compliance_scan for the deep knowledge-base audit. Free, read-only,\n    no API key required, no credits deducted.\n\n    Args:\n        text: raw listing title + bullets + description (required).\n        marketplace: marketplace code, US/DE/ES/FR/IT/JP/AE/SA/UK (default US).\n        lang: zh or en (default en).\n        email: optional lead email for a confirmation message and lead capture.",
      "inputSchema": {
        "properties": {
          "email": {
            "default": "",
            "title": "Email",
            "type": "string"
          },
          "lang": {
            "default": "en",
            "title": "Lang",
            "type": "string"
          },
          "marketplace": {
            "default": "US",
            "title": "Marketplace",
            "type": "string"
          },
          "text": {
            "title": "Text",
            "type": "string"
          }
        },
        "required": [
          "text"
        ],
        "title": "ai_readiness_checkArguments",
        "type": "object"
      },
      "name": "ai_readiness_check"
    },
    {
      "description": "Analyze a negative Amazon review for root cause and a suggested response (async; 2 credits).\n\n    Extracts the underlying issue from a critical review and drafts a brand-appropriate response\n    angle. Use this after a negative review appears, to decide how to reply. Do NOT use it to\n    generate a listing or an appeal - use generate_listing or generate_poa for those. Read-only;\n    deducts 2 credits; runs asynchronously, poll for the result.\n\n    Args:\n        text: the negative review text (required).\n        marketplace: marketplace code (default US).\n        lang: zh or en (default en).",
      "inputSchema": {
        "properties": {
          "lang": {
            "default": "en",
            "title": "Lang",
            "type": "string"
          },
          "marketplace": {
            "default": "US",
            "title": "Marketplace",
            "type": "string"
          },
          "text": {
            "title": "Text",
            "type": "string"
          }
        },
        "required": [
          "text"
        ],
        "title": "analyze_reviewArguments",
        "type": "object"
      },
      "name": "analyze_review"
    },
    {
      "description": "Quick pre-publish compliance gate before generating a listing.\n\n    Fast, free scan for obvious red-line words and category risks. Returns a shallow\n    pass/fail-style result, not a full audit.\n\n    Use this as a cheap pre-check right before generation. Do NOT use it for a complete risk\n    report - use compliance_scan for the deep knowledge-base audit. Read-only; requires an API\n    key; no credits deducted.\n\n    Args:\n        text: listing copy (required).\n        lang: zh or en (default en).\n        category: optional category hint, e.g. electronics or apparel.",
      "inputSchema": {
        "properties": {
          "category": {
            "default": "",
            "title": "Category",
            "type": "string"
          },
          "lang": {
            "default": "en",
            "title": "Lang",
            "type": "string"
          },
          "text": {
            "title": "Text",
            "type": "string"
          }
        },
        "required": [
          "text"
        ],
        "title": "compliance_checkArguments",
        "type": "object"
      },
      "name": "compliance_check"
    },
    {
      "description": "Deep knowledge-base compliance audit (async; 2 credits).\n\n    Produces a thorough, written risk report covering prohibited words, IP, category\n    mismatches, GPSR and more, backed by a private 15-year compliance knowledge base.\n\n    Use this when you need a complete, actionable compliance report. Do NOT use it for a quick\n    pre-publish check - use compliance_check for that. Read-only; deducts 2 credits; runs\n    asynchronously, poll for the result.\n\n    Args:\n        text: listing title/bullets/description (required).\n        marketplace: marketplace code (default US).\n        category: optional category hint.\n        lang: zh or en (default en).\n        images: optional list of up to 5 data:image base64 strings, each < 4MB.",
      "inputSchema": {
        "properties": {
          "category": {
            "default": "",
            "title": "Category",
            "type": "string"
          },
          "images": {
            "default": null,
            "items": {},
            "title": "Images",
            "type": "array"
          },
          "lang": {
            "default": "en",
            "title": "Lang",
            "type": "string"
          },
          "marketplace": {
            "default": "US",
            "title": "Marketplace",
            "type": "string"
          },
          "text": {
            "title": "Text",
            "type": "string"
          }
        },
        "required": [
          "text"
        ],
        "title": "compliance_scanArguments",
        "type": "object"
      },
      "name": "compliance_scan"
    },
    {
      "description": "Expand a one-sentence product description into structured listing fields (free; API key; no credits).\n\n    Turns casual, spoken product copy into structured fields (title, bullets, features) that feed\n    the listing generator. Use this as a low-friction starting point when you only have a rough\n    sentence. Do NOT use it to produce a final optimized listing - use generate_listing for that.\n    Read-only; free; no credits deducted.\n\n    Args:\n        sentence: one-sentence product description, 4-1000 characters (required).\n        lang: zh or en (default en).",
      "inputSchema": {
        "properties": {
          "lang": {
            "default": "en",
            "title": "Lang",
            "type": "string"
          },
          "sentence": {
            "title": "Sentence",
            "type": "string"
          }
        },
        "required": [
          "sentence"
        ],
        "title": "fill_from_sentenceArguments",
        "type": "object"
      },
      "name": "fill_from_sentence"
    },
    {
      "description": "Generate a high-conversion Amazon listing - title + bullets + description (async; 1 credit per marketplace).\n\n    Produces A9-optimized copy that respects per-marketplace character limits. Use this to create\n    a full listing from product facts. Not free - deducts 1 credit per selected marketplace and\n    runs asynchronously, poll for the result. Read-only: it drafts text and does not publish.\n\n    Args:\n        cn_name: product Chinese name (required).\n        sku: product SKU (required).\n        marketplaces: optional list of marketplace codes; defaults to all 9.\n        price: optional product price.\n        lang: zh or en (default en).",
      "inputSchema": {
        "properties": {
          "cn_name": {
            "title": "Cn Name",
            "type": "string"
          },
          "lang": {
            "default": "en",
            "title": "Lang",
            "type": "string"
          },
          "marketplaces": {
            "default": null,
            "items": {},
            "title": "Marketplaces",
            "type": "array"
          },
          "price": {
            "default": "",
            "title": "Price",
            "type": "string"
          },
          "sku": {
            "title": "Sku",
            "type": "string"
          }
        },
        "required": [
          "cn_name",
          "sku"
        ],
        "title": "generate_listingArguments",
        "type": "object"
      },
      "name": "generate_listing"
    },
    {
      "description": "Draft a submission-ready Plan of Action (POA) from an Amazon violation notice (async; 4 credits).\n\n    Converts a suspension or removal email into a structured POA appeal. Use this when a listing\n    or account is suppressed. Not free - deducts 4 credits and runs asynchronously, poll for the\n    result. Read-only: it drafts text and does not submit anything to Amazon.\n\n    Args:\n        text: violation notice or removal email text (required).\n        marketplace: marketplace code (default US).\n        lang: zh or en (default en).\n        violation_type: optional, e.g. ip_complaint / authenticity / policy.",
      "inputSchema": {
        "properties": {
          "lang": {
            "default": "en",
            "title": "Lang",
            "type": "string"
          },
          "marketplace": {
            "default": "US",
            "title": "Marketplace",
            "type": "string"
          },
          "text": {
            "title": "Text",
            "type": "string"
          },
          "violation_type": {
            "default": "",
            "title": "Violation Type",
            "type": "string"
          }
        },
        "required": [
          "text"
        ],
        "title": "generate_poaArguments",
        "type": "object"
      },
      "name": "generate_poa"
    }
  ]
}

What the leaf hashes

{"body":"97743ce4d20135f3cf9ca837292698630b421279d1e67f0ae965045b0d081374","id":276864,"kind":"mcp.tools","method":"probe.mcp/1","observed_us":1793041610035600,"prev":null,"subject":"4a8bd227-8532-4016-bd2b-6dc280fb1e64","subject_kind":"endpoint"}

Proof

Fold these into the leaf in order, hashing each sorted pair with keccak256, to reach the root of 2026-10-26 (296695 rows).

1. 0x2c8be6bffe28f5fc8f6097a16d35826cc3bbcbf9cc9bc1563d4250e81c8a8c3f
2. 0x360db3c04adf897457c5f05994be7bef1944d5552ff43c38fc46a039eb90fbc5
3. 0xe7079e45fa33a64830759f9bf75f76efe818e53747a62961f1a6090545c51812
4. 0x24fcd17ea16b2dd1a8cdfdceb6121da3d8250b8403da48642fce9ea40d72c641
5. 0xb59ec9e7ead4bd1385e33a6dbac5f02cd8929dfc1df0068e0a972edb2cc0d604
6. 0xef835f9ad6b8b1bb0eeb3792545bcc297986cea8ce0d4cb00275b0976c5a40cb
7. 0x8da0f03bd5a737b8295b3bc256dde51c3c235852c87023f974ae7f97dfa1c5cd
8. 0xc3d2d17d402b27bf760d5c7da6751003c469aae1e990825f755dc8b8371ad08c
9. 0x7420dba81dac91978bdf01f95a02b6ae12fba03502cf439be566afd5217ef304
10. 0x92ae512ac303ebf33e234dd8900de38bc100cacd7e3e6d4585dab96308c421ca
11. 0x9f7d3cc11f13fc54c5221c4babc2273f69d77c4b805c79df41df74cfb0975d6c
12. 0xf89d9951e0a8e08b9cf11177a1f4e0fd86d9facc5dfecaabbfe564675ebefef7
13. 0xb87e8b32cda179c9e632e227bbd8a6d1f6f61d9d81bb92dbd4dd835d2b3b5caa
14. 0x683955a5b2250ad50568d022a6580d8739b6aff1356779d72a773b088e7c289c
15. 0x610738e0c43687aded83c613c73de8b5226c73c375858a167436461c522ea213
16. 0x06d71bfa7d8b41d62c8836efc2c601e1cc3772d9453596b2ae4950887f3310ca
17. 0x8c756542e482157df0c36464d75c937b845e4423dbbcfad08f0cf8d5a16b9a13
18. 0x20704208aa6923d6b2450ef74ede87a6e0265d46447926038fe9506e9e154b2d
19. 0x9b9ce5b74b9bab5121612ef6b64920a8d453cbf311096a423eef9702747e868a

Day root

0xf43e8dcc3d451a4788b26be904c55e8c4c0fa75c9666796c51db38d37e2c0df8

Chained

0x702d5ac508f08b0d1accf36af25822d8e5660367494edd17a642fb61e158a57a

Published

On Arc Testnet, block 69251483: 0x495162f8936e19f0039918feebe6c307f327cd6bf8c37219620b1970041fa9ae