Evidence row 1850496

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 blocks on AI training crawlers, a Content-Signal of ai-train=no, 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":"a6c34dccc6e5b6ca76724e7c90cd0e988cf80cb87857597a2c60a7561bc19a2a","id":1850496,"kind":"mcp.tools","method":"probe.mcp/1","observed_us":1793578096818795,"prev":1009169,"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 2026-11-02 (444186 rows).

1. 0xd04fa0ed7d85acf2e4cf49d069f5b2873abdbf15e5d3b83800c71a97af529f64
2. 0xf68d1c67dd2bd58fc3c7721a9a7acbafcc587b5d2253c7afde1294167b32ba3a
3. 0x4277e59726b5219b1f68404056d005a2075865588c95de4a01738eefd6356927
4. 0x78ff171024483bc1d53fa1827646f54f8ca60912e4edd2c1d7b2aaa07c19942b
5. 0xd08705c84fef4c4c1d27c98e4512071c5b683c4d1d15ea592d79c4955c0de75b
6. 0x94a482d84ca805b7dd66454a8d1d39b83b11acb83bb0eff78c79b9d329ffc7c0
7. 0x3bb1a21919ccf0194fc7c75de2aff2d5bb118667cb2671e5a45d824298c27bd8
8. 0x4952b84f43da402048402a7a8b8fa08bc27baf50d4996309818a0bf730bdf43d
9. 0xa27eb11850f2699c8facb748b5e30d4888bdad78ecbb3029b3a59f63958af055
10. 0x772a7d62e7f68f5fefc0c14f50afbed7127ff6e05fdec047ee9172692eb4a101
11. 0xa3e59ea5049f0fb500b94c99db5bf2f100811da0dccd9ed6404f70897aa1e5fd
12. 0x958b4a44f39e6f2ea70e91d6ebf20620245d2b968ca9c28f1b19f03aa4f5e55f
13. 0xc68903bc934de8372c6b911a71856ad742aaac2d4b2de9ce90e84a085da4373d
14. 0x0a363925e7ecbf308dcd4c5e94385984a43aafeb977e419f632e8dc2019e0553
15. 0xa9134f4ed9cec34e13ba243b6a51db73753dbbb3b48c63bd82b22694813d2b02
16. 0x7ddefa7d79e7640c3fed21ee4472689415182c41a0258ab04a81d60495f4c40a
17. 0xdb8bd933619a267f9790faac9839143d202be46af284371cc369cdf683f897ba
18. 0x98cbbf2c68ab4c0b4db782aeb88bea1f30b02178cf8b5d3d24a931d19492ea6d
19. 0x252cce55f1a3d5391c16f82d7f62494429a5f69b03486d4cfcfb7b2c96d6c884

Day root

0x78fecaabbe319e56f5c4b80fc02e2052e198ad24781da8832a475ff65f2002d1

Chained

0xdc35957db5d1371ca382b9152878ffece28ec7c5cb4f98fd71ddb367f8b999b2

Published

On Arc Testnet, block 70432815: 0x56b814612eb2e3e544153df3e91e1f7199106d1e430c7d8c6b653feeb9ebe9af