Evidence row 562537

What was observed

{
  "count": 2,
  "tools": [
    {
      "description": "Use this when a business owner asks whether ChatGPT or AI recommends their business, whether AI tools like ChatGPT or Perplexity mention or suggest their business, why AI recommends their competitors instead, or how to get their business recommended by AI. It runs a real check: it asks ChatGPT, Perplexity and Gemini up to 3 questions a customer would ask when looking for this kind of business in the area the business serves, and reports whether each engine named the business, which businesses they named instead, and one fix based on the pages the answers cited. Timing: a check usually finishes in under a minute. This tool waits up to about 20 seconds and returns the result when it is ready; otherwise it returns a check_id with the status queued or running, and get_ai_recommendation_result returns the result for that check_id. A saved result for the same business is returned right away. Business details are sent to Continuum (uxcontinuum.com) to run the check. Results are shown in the conversation. Not covered: general SEO or Google ranking questions, writing marketing copy or ads, looking up or checking a person (it checks businesses only), and general questions about how AI search works. Limits: 3 buyer questions per website per day; saved results are reused for 7 days.",
      "inputSchema": {
        "additionalProperties": false,
        "properties": {
          "business_name": {
            "description": "The business name as customers know it, for example \"Joe's Plumbing\".",
            "maxLength": 100,
            "minLength": 2,
            "type": "string"
          },
          "category": {
            "description": "What the business is, in the words a customer would use to look for one, for example \"plumber\", \"wedding photographer\" or \"HVAC company\". Not a slogan.",
            "maxLength": 60,
            "minLength": 2,
            "type": "string"
          },
          "email": {
            "description": "Optional. The owner's email address, if they want Continuum to send this result and follow up.",
            "maxLength": 254,
            "minLength": 6,
            "type": "string"
          },
          "service_area": {
            "description": "The area the business serves, as its customers would name it, for example \"Austin, TX\". This describes the business, not the user's own location. Empty when the business serves customers anywhere online.",
            "maxLength": 80,
            "type": "string"
          },
          "website": {
            "description": "The business website, for example \"joesplumbing.com\". Used to recognise the business in answers and to keep its daily limit.",
            "maxLength": 253,
            "minLength": 4,
            "type": "string"
          }
        },
        "required": [
          "business_name",
          "website",
          "category"
        ],
        "type": "object"
      },
      "name": "check_ai_recommendation"
    },
    {
      "description": "Use this to read the result of a check started with check_ai_recommendation, using the check_id it returned. Returns the status (queued, running, done, failed or refused). When done: for ChatGPT, Perplexity and Gemini, how many of their answers named the business, which businesses they named instead, and one fix. While a check is queued or running, poll_after_s is the suggested wait in seconds before reading it again. It waits up to about 20 seconds for a running check to finish. It does not start a new check.",
      "inputSchema": {
        "additionalProperties": false,
        "properties": {
          "check_id": {
            "description": "The check_id returned by check_ai_recommendation.",
            "pattern": "^[0-9a-f]{32}$",
            "type": "string"
          }
        },
        "required": [
          "check_id"
        ],
        "type": "object"
      },
      "name": "get_ai_recommendation_result"
    }
  ]
}

What the leaf hashes

{"body":"99f6b4d220237110322aed282b77ddd0252a98ee89aea39855332ec6e7333703","id":562537,"kind":"mcp.tools","method":"probe.mcp/1","observed_us":1793137426208009,"prev":null,"subject":"e0a96aac-4df7-4f22-8961-4471cc91de43","subject_kind":"endpoint"}

Proof

Fold these into the leaf in order, hashing each sorted pair with keccak256, to reach the root of 2026-10-27 (273461 rows).

1. 0x295196fd28d16426675e2dfa3c3c4ffae9f3e40d804a733c8ad49df6e01753c7
2. 0xbe7116b1b2c8c62bb1ce9cf0a810e8a0d2c62608e3e77bb1d474475f7ec76f8d
3. 0x2533f034701be5b38f7508505ca00dfa01d6ae9818352262f69e566df7c79fe5
4. 0x5bed9afaf47faae0341c2b6a4ed7b5481ef46a8b8846d78be3175a98523ab2d0
5. 0x0bb3cd2632a9f207ea86b9328012007497edb2b15f3c26973c07086140f72cf1
6. 0xb2531eddaebd0c14b60c842a4af50a01480526f28e15ac863627b12fcb239542
7. 0x6a96820d66e274d270207d43685abe5ac2cae3c2ec580ddf50681b645c063c74
8. 0x21e5e01541f8432a4e32b73556b13ca70a5850ebf5f2274a60dd8e6f501b9f54
9. 0x6a3b7b01fd91fd7830f8e162967c33aaa576c842a9e880a37b94d321f6aedd48
10. 0x9a20ca0b3dcc5486368012c7eb69b5df7e62d4d019646dad58fe4cf6f87499ae
11. 0x64977be4349e1af29930fcfd7d89ebbb219bc4a1a076ecb37395cda952cbeb6b
12. 0x53be2d72842ed384672ee78d06cbad599558474b78db9065a3b26f87d53522fa
13. 0x28be2f2303fcac4decdb6323b0f4ee8f8a31038beb758eaafc604a98626292e4
14. 0x8ff18b1523f9fbdd16a5796cbe184b46854c06256508af8de5509b598370c725
15. 0xa383a1cfe9b3f6af67eb4c7642564b9424d46d7fca90e7ba175d549f8b67dd0c
16. 0x52efa60e0c3d8525486d2310eb3b6b566c9677c64fd1f819edb072cf101bcbdb
17. 0x22c1fb9487ee0b9732fa017a0cc321eb27a60c08450d112f755089796e12cb1c
18. 0xa20d48798a34aba12eb9c7297722982d910f06c148f5e27ac59307422964a585
19. 0x9fc354c0ea3efc3f1b6548bb77ea902a50f2c9647647ea61592efce2bd795699

Day root

0xdd77e5e99da32c3f154180d63e3c3e6fe11e896a2c6896412ef6b5c4bcf7e795

Chained

0x23bb4dfd09f55c15ac3962f53578e02c6114d5bfd9bb451cf8794fb6e2817ef4

Published

On Arc Testnet, block 69414147: 0xb2ebd83aafeb765a0ce66e76aefb133ed06606c98038fd56191e2bcd1dcb77b4