Evidence row 301740

What was observed

{
  "count": 7,
  "tools": [
    {
      "description": "Find all entities of a specific schema.org type (exact type name, for example Service, Person, DefinedTerm).",
      "inputSchema": {
        "properties": {
          "type": {
            "type": "string"
          }
        },
        "required": [
          "type"
        ],
        "type": "object"
      },
      "name": "find_by_type"
    },
    {
      "description": "Find and rank schema.org entities from natural language, tolerating typos and alternative names. Returns bounded entity context with relationship IDs; use get_entity for full detail. The optional detail argument is summary (for each hit: id, type, name, url, the start of the description and the match score) or full (every property and relationship). A client on protocol 2026-07-28 or later gets summary unless it asks for full; an older client gets full unless it asks for summary.",
      "inputSchema": {
        "properties": {
          "detail": {
            "enum": [
              "summary",
              "full"
            ],
            "type": "string"
          },
          "limit": {
            "default": 5,
            "maximum": 10,
            "minimum": 1,
            "type": "integer"
          },
          "query": {
            "type": "string"
          },
          "type": {
            "type": "string"
          },
          "weights": {
            "properties": {
              "entityTypes": {
                "type": "object"
              },
              "predicates": {
                "type": "object"
              }
            },
            "type": "object"
          }
        },
        "required": [
          "query"
        ],
        "type": "object"
      },
      "name": "fuzzy_find_and_traverse"
    },
    {
      "description": "Return passages of the VISEON articles word for word, each with a link to the passage itself, the article it is part of and how much of the query it holds, so an answer can quote and cite them. query: the distinctive words asked about (a name, a term, a phrase the article uses). article: optional, an article's @id, page url or title, to look inside that article only. limit: 1 to 5 passages, default 3. The text is verbatim and unaltered: no model has read or paraphrased it. It matches the article's own words, so ask in the words the article would use; for what the graph says about an article (description, author, date, topics) use fuzzy_find_and_traverse and get_entity.",
      "inputSchema": {
        "properties": {
          "article": {
            "type": "string"
          },
          "limit": {
            "default": 3,
            "maximum": 5,
            "minimum": 1,
            "type": "integer"
          },
          "query": {
            "type": "string"
          }
        },
        "required": [
          "query"
        ],
        "type": "object"
      },
      "name": "get_citable_passages"
    },
    {
      "description": "Get complete schema.org entity data by URL (its @id), including relationships the graph holds in either direction.",
      "inputSchema": {
        "properties": {
          "url": {
            "type": "string"
          }
        },
        "required": [
          "url"
        ],
        "type": "object"
      },
      "name": "get_entity"
    },
    {
      "description": "Find entities connected to a subject: one row per property value, with object_type 'uri' for links to other entities and 'literal' for values. Optionally filter by predicate.",
      "inputSchema": {
        "properties": {
          "predicate": {
            "type": "string"
          },
          "subject": {
            "type": "string"
          }
        },
        "required": [
          "subject"
        ],
        "type": "object"
      },
      "name": "query_relationships"
    },
    {
      "description": "Substring search across the text of every entity; returns up to 20 entity URLs.",
      "inputSchema": {
        "properties": {
          "query": {
            "type": "string"
          }
        },
        "required": [
          "query"
        ],
        "type": "object"
      },
      "name": "search_content"
    },
    {
      "description": "Walk the knowledge graph from one entity in a single call. start: an entity @id or a name (resolved like fuzzy_find_and_traverse). path: one entry per hop, each a predicate or a list of predicates; prefix a predicate with ^ to follow it backwards (e.g. ^worksFor finds who works for an organisation). Literal values (dates, names, emails) named in a hop are returned in values. Example, who founded the company Hamza works for and when: start \"Hamza Ashraf\", path [[\"worksFor\"], [\"founder\", \"foundingDate\"]]. Without path, follows every link for maxHops (1-3, default 2) and returns links only, with a budget of 100 rows shared between hops, so hubs are cut short rather than hiding later hops (use a path to go deeper: a path call may return up to 400 rows). Returns edges [{hop, from, predicate, to, to_name, to_types}], values [{hop, subject, subject_name, predicate, value}], truncated and truncated_hops (the hops that hit a limit).",
      "inputSchema": {
        "properties": {
          "maxHops": {
            "default": 2,
            "maximum": 3,
            "minimum": 1,
            "type": "integer"
          },
          "path": {
            "items": {
              "anyOf": [
                {
                  "type": "string"
                },
                {
                  "items": {
                    "type": "string"
                  },
                  "type": "array"
                }
              ]
            },
            "maxItems": 3,
            "type": "array"
          },
          "start": {
            "type": "string"
          }
        },
        "required": [
          "start"
        ],
        "type": "object"
      },
      "name": "traverse_path"
    }
  ]
}

What the leaf hashes

{"body":"15f6c3eda198fc7478fd29265b0e872c7bcd0a207eacb4370d176e41e6bf23e1","id":301740,"kind":"mcp.tools","method":"probe.mcp/1","observed_us":1793054256231247,"prev":null,"subject":"bb331bfc-8a9f-4c91-8bb2-0b3ed45e28ae","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. 0x93982a32bbc5d55e82005d17bbc256d180d2d776bc42fb3b064f3d98c60ae176
2. 0x8e2a4f1dee5682ad89a887c04466357b9f15c0d228e6512286889aef2630e479
3. 0x5de44251b1cf8698672ca286d9d428a98092d9c3360be2db1b19bfe3edb9d4a4
4. 0x7aae2ddb7b04e330520865e6e9b17b1ad7926e30597dee20dd829b5d3c2a4177
5. 0x3e084844bf0033401626a3e73e4c666955744cf52e2b0c61eee260632ae70781
6. 0xc40f250098dc3fda893360617a2ed0874c93117c8b9c8cdbe903d17a40a2b9ec
7. 0x21ac38178b0900a44217518ff0e1974e4f21bcf978b60ee54a2a96d9455b3e43
8. 0x7a551024620833b5701814d9551a08404cf5e8dfb760c7abf58f3ce06931d1c9
9. 0x9c935e0d4bfff802c2c603de72b42f45c679ee9ab100bb7283c50b183356680c
10. 0x1452bac1c16ed1a13fd24d016370307a5402f74d73cad5641a5508fdb4d2c5cb
11. 0xcd52656344e352446858a7d55feb9336b07dd3ad98715c122aaf69d4eb3d6ca8
12. 0x3b55f6d5345bce3e1332e014f19526d25328fb6283d3518837a015c3621c0d1c
13. 0x45c1d3062bbe98a6b1540c7e2ccf6516eedd6ef07fdfeb1ba77ee6272970447c
14. 0xf0edabbb9ba0d643e5505d19d07637eb6f6aed0a1c7356af43ae7d84ae9ef0c5
15. 0xf0bd63870f0bb378fa300bd33563050dcb7b3e02b44eb3ee550c1c534ec4b246
16. 0x1ac20d30773e1f4f2f811540e9a99b5cca9177c9e2e029acc0d80cf3f1d11f0f
17. 0xd21942ae3214eff88e08ab04884fd4b11958b51af9a0741b7668e2d07a2c56c9
18. 0x9a9024edb5069d88a324facb310a5ce8e495214bbaa968f077bede437390194f
19. 0x9b9ce5b74b9bab5121612ef6b64920a8d453cbf311096a423eef9702747e868a

Day root

0xf43e8dcc3d451a4788b26be904c55e8c4c0fa75c9666796c51db38d37e2c0df8

Chained

0x702d5ac508f08b0d1accf36af25822d8e5660367494edd17a642fb61e158a57a

Published

On Arc Testnet, block 69251483: 0x495162f8936e19f0039918feebe6c307f327cd6bf8c37219620b1970041fa9ae