Evidence row 1009169

What was observed

{
  "count": 8,
  "tools": [
    {
      "description": "Does robots.txt allow fetching this exact URL? Fetches the live robots.txt and applies the longest-match rule for a user agent (default: * plus GPTBot, ClaudeBot, Google-Extended, CCBot, PerplexityBot). Returns allowed or not, the rule that decided it, and any crawl delay. A courtesy check, not a legal opinion.",
      "inputSchema": {
        "properties": {
          "url": {
            "description": "The full URL you want to fetch",
            "type": "string"
          },
          "user_agent": {
            "description": "optional, e.g. GPTBot",
            "type": "string"
          }
        },
        "required": [
          "url"
        ],
        "type": "object"
      },
      "name": "can_fetch"
    },
    {
      "description": "Can I scrape this site, and how? Returns a verdict (open, guarded, hostile, blocked), a hostility score, a recommended approach with steps and risks, bot protections, robots.txt and AI-crawler policy, machine-readable surfaces (sitemap, llms.txt, JSON-LD), and eu_ai_training: the signals the EDPB draft Guidelines 03/2026 on web scraping for generative AI count against collecting personal data (robots.txt, ai.txt, CAPTCHAs, login walls), as none, some or strong. Signals, not legal advice. Uses a weekly-probed dataset of several hundred sites; for any other domain it probes live from a datacenter IP.",
      "inputSchema": {
        "properties": {
          "domain": {
            "description": "A domain such as reddit.com",
            "type": "string"
          }
        },
        "required": [
          "domain"
        ],
        "type": "object"
      },
      "name": "check_site"
    },
    {
      "description": "What should I build with this site, and how? Give a domain and, optionally, a goal in plain words (for example \"price alerts\", \"sales leads\", \"new jobs\", \"change log\", \"knowledge base\"). Returns the best-matching workflow for that site: what it does, its cadence, a trigger, collect, shape, store, act pipeline, the fields to keep, and step-by-step build recipes for n8n, Make, Zapier and Apify (or just one with platform). Workflows marked found_on_this_site come from what the weekly probe saw on the site itself. Also returns the other workflows, the golden data, and the guardrails.",
      "inputSchema": {
        "properties": {
          "domain": {
            "description": "A domain such as amazon.com",
            "type": "string"
          },
          "goal": {
            "description": "optional, what you want to achieve",
            "type": "string"
          },
          "platform": {
            "description": "optional, only return this platform (n8n also returns n8n_workflow, an importable file):'s recipe",
            "enum": [
              "n8n",
              "make",
              "zapier",
              "apify"
            ],
            "type": "string"
          }
        },
        "required": [
          "domain"
        ],
        "type": "object"
      },
      "name": "get_playbook"
    },
    {
      "description": "List the sites in the dataset, optionally by category or verdict. Use it to find easy targets or to see what is covered.",
      "inputSchema": {
        "properties": {
          "category": {
            "description": "e.g. shop, jobs, news, property, travel, gov",
            "type": "string"
          },
          "limit": {
            "description": "default 50, max 200",
            "type": "integer"
          },
          "verdict": {
            "enum": [
              "open",
              "guarded",
              "hostile",
              "blocked"
            ],
            "type": "string"
          }
        },
        "type": "object"
      },
      "name": "list_sites"
    },
    {
      "description": "Find sites in the dataset by a word: a name, host, category (jobs, shop, news, gov), country or verdict. Use it to discover what is covered before calling check_site.",
      "inputSchema": {
        "properties": {
          "limit": {
            "description": "default 25, max 100",
            "type": "integer"
          },
          "query": {
            "type": "string"
          }
        },
        "required": [
          "query"
        ],
        "type": "object"
      },
      "name": "search_sites"
    },
    {
      "description": "Headline numbers across the dataset: verdict mix, how many sites block AI crawlers (and which), protection vendors, by category and country.",
      "inputSchema": {
        "properties": {},
        "type": "object"
      },
      "name": "summary"
    },
    {
      "description": "Draw a page's anatomy as an Excalidraw diagram: its regions, the ranked ways to get its data, proof rows, the best route and what the page is made of. Returns a download link for the .excalidraw file (give it to the user; it opens at excalidraw.com or in Obsidian) and a file_name. With include_scene, also the scene itself (a few hundred KB).",
      "inputSchema": {
        "properties": {
          "include_scene": {
            "description": "also return the full Excalidraw scene JSON (large). Default false: just the download link",
            "type": "boolean"
          },
          "url": {
            "description": "Full page URL, e.g. https://example.com/products",
            "type": "string"
          }
        },
        "required": [
          "url"
        ],
        "type": "object"
      },
      "name": "xray_diagram"
    },
    {
      "description": "How do I extract the data from THIS page? Fetches one public page and returns a ranked extraction plan: embedded JSON, JSON-LD, repeated records with CSS selectors and field names, tables, feeds, hidden API calls. Each source has a reliability, proof rows pulled from the real page, and the plan notes pagination and blockers. Set include_code for a starter Python script. Works on the raw HTML only, so JavaScript-built pages report a blocker instead of data. Use check_site first to learn whether the site allows scraping at all.",
      "inputSchema": {
        "properties": {
          "include_code": {
            "description": "also return a starter Python script",
            "type": "boolean"
          },
          "url": {
            "description": "Full page URL, e.g. https://example.com/products",
            "type": "string"
          }
        },
        "required": [
          "url"
        ],
        "type": "object"
      },
      "name": "xray_page"
    }
  ]
}

What the leaf hashes

{"body":"4c1d787d651d1c3e882bcafd0960ce7ff528286020bf7d30802211127342e9ee","id":1009169,"kind":"mcp.tools","method":"probe.mcp/1","observed_us":1793265341369863,"prev":null,"subject":"5ac4792b-f812-4ee5-8b5d-1a0471cd3473","subject_kind":"endpoint"}

Proof

Fold these into the leaf in order, hashing each sorted pair with keccak256, to reach the root of 2026-10-29 (242798 rows).

1. 0x7819f3f2a306e918983305f5f2c515266ef5b0c5cc8a2fa8e2864e37831b72c4
2. 0x524591f5ca7eb9eeb4de564e9461e8bfa55f7611843beb0c1802bd193508419c
3. 0xbc34e056ceb2b00f7834b6eaf5ae4f5d87aee9584148d47c749568d8f29a75cc
4. 0xc71e55a7d82a477aaf8db327abb3e43a8968b2c52ab8acf65b331f303a0d118e
5. 0x82048844eaf329539381c8e4c92cad0138b75235556d74ba30812c5019857d8a
6. 0xf960157b8f10958878742085fee6c2b69d1af83dd1ea74216641e254fbe91d7e
7. 0xcd6cafb2417ce86d5a1b55258fe62f1dacd93a5d701a0b860c4feee2eaabfceb
8. 0x4094af0e38aef69f41f9ef6ca424709da2270550f513e28e67f1350f254f90f9
9. 0x5884b16a3eec301617b07990c42bb4a093e30ecd46fa7784cedfb74fd5613db1
10. 0x07e0c84d917943facd277b591b19a4104398984df43cbbe2fa056e0a04c8067e
11. 0xeb4c89f10ecf7036c1a5612800e96a9fdbf56f9ae92f4d338413d367ff69ebbf
12. 0x63a11cd9e65d16993c0e702ce6d7bda551e5a72e62115aaf790a2e3519c7fac6
13. 0xc0f950bf9c872c684cf701ee03b280e31f9fd7a44bed4df783964fd0df78d0c2
14. 0xa07c03f1ff4a33c8a0105ab761d762ba211920a4e09af3e5ae84b51a3c4cf51b
15. 0x60b5befbd81f025904c8a3936b7ba86e3245ad5b37c615d01d42aa3227e3ed2e
16. 0xe2d603cd6f987214550319ad4fa39952b6098bc5c1b20893b6b80e8d82a79934
17. 0x91f12a2db1e2c1be07cbe4de3981a37a33051a36d3c3eee5a260804add6d1d75
18. 0xd56e61af23709ed9f460471e3c08400c0142ee83e1fd9057e46350d12edf9c07

Day root

0xc050555c6b023f2aefb6536efb9f0760b92c30a0d06c572b7daf63db259ea17c

Chained

0x4cde8bae2042512e1bec11650628f0293556311cc4ea74d99ffddf02b671a7d6

Published

On Arc Testnet, block 69753317: 0x9e85c340732f416e431b111fae72f7624870125f7f379af2732eb6c2ba8cf6a1