Evidence row 563353

What was observed

{
  "count": 6,
  "tools": [
    {
      "description": "The full text of an article, for when lookup()'s summary is not enough — sections as a JSON array, infobox as key/value facts, no HTML or wikitext to parse. Pass `sections` to pull only the parts you need (e.g. [\"Early life\"]) and `max_chars` to cap the payload; both exist because a long article will otherwise flood your context.",
      "inputSchema": {
        "properties": {
          "corpus": {
            "description": "Which corpus to read from. Defaults to wikipedia.",
            "enum": [
              "wikipedia",
              "wikiquote",
              "wikibooks",
              "wikivoyage",
              "wikiversity"
            ],
            "type": "string"
          },
          "max_chars": {
            "description": "Optional cap on total section text returned.",
            "type": "integer"
          },
          "sections": {
            "description": "Optional section names to include (substring match, case-insensitive). Omit for the whole article.",
            "items": {
              "type": "string"
            },
            "type": "array"
          },
          "title": {
            "description": "Article title, alias, or Q-id.",
            "type": "string"
          }
        },
        "required": [
          "title"
        ],
        "type": "object"
      },
      "name": "article"
    },
    {
      "description": "What a word means, in thousands of languages — 8.15M dictionary entries with senses, part of speech, etymology and pronunciation. Covers what a general model is weakest at: historical languages (Old English, Gothic, Ancient Greek, Middle French) and hundreds of regional and indigenous ones. A single spelling often has entries in many languages and you get all of them — `hund` returns Danish, Gothic, Icelandic, Middle English and more — or pass `language` to narrow, `pos` for one part of speech. Use this for words and lookup() for things: define(\"java\") gives the word in eight languages, lookup(\"Java\") gives the island.",
      "inputSchema": {
        "properties": {
          "language": {
            "description": "Optional language name as Wiktionary spells it, e.g. \"English\", \"Latin\", \"Spanish\".",
            "type": "string"
          },
          "pos": {
            "description": "Optional part of speech filter, e.g. \"Noun\", \"Verb\", \"Adjective\".",
            "type": "string"
          },
          "word": {
            "description": "The word or phrase to define.",
            "type": "string"
          }
        },
        "required": [
          "word"
        ],
        "type": "object"
      },
      "name": "define"
    },
    {
      "description": "Facts about any named thing — person, company, place, species, event, concept. Returns structured fields (dates, identifiers, relationships) plus a ~200-token summary, drawn from 10.2M entity records. Prefer this over fetching an encyclopedia page: the HTML costs ~15,000 tokens to recover ~500 tokens of fact. Resolves aliases and Wikidata Q-ids, so \"Apple\", \"Apple Inc\" and \"Q312\" all reach the same entity. Free, no key.",
      "inputSchema": {
        "properties": {
          "corpus": {
            "description": "Which corpus to look in. Defaults to wikipedia. Use wikivoyage for travel guides, wikiquote for quotations, wikibooks for textbooks, wikiversity for course material.",
            "enum": [
              "wikipedia",
              "wikiquote",
              "wikibooks",
              "wikivoyage",
              "wikiversity"
            ],
            "type": "string"
          },
          "entity": {
            "description": "Entity name, Wikipedia title, alias, or Wikidata Q-id (e.g. \"Tim Cook\", \"Q312\").",
            "type": "string"
          }
        },
        "required": [
          "entity"
        ],
        "type": "object"
      },
      "name": "lookup"
    },
    {
      "description": "Academic paper metadata from 57M+ works — title, abstract, authors, citations, DOI and open access URL. Covers every field: CS, medicine, physics, economics, biology, and more. Search by keywords (e.g. \"transformer attention\"), by topic name (e.g. \"machine learning\"), or by OpenAlex topic ID. Returns papers sorted by citation count. Source: OpenAlex (CC0 metadata). Use this when the user needs scholarly references, citation counts, or research context that Wikipedia does not cover.",
      "inputSchema": {
        "properties": {
          "limit": {
            "description": "Maximum papers to return, 1-20 (default 5).",
            "type": "integer"
          },
          "query": {
            "description": "Keywords to search for. Matches topic names to find the right research area, then filters by title and abstract. Example: \"transformer attention\", \"CRISPR gene editing\".",
            "type": "string"
          },
          "topic": {
            "description": "Topic name or OpenAlex topic ID. Names are resolved automatically (e.g. \"machine learning\" → T10135). Use an ID like \"T10135\" if you already know it. Optional — query alone works.",
            "type": "string"
          },
          "year": {
            "description": "Publication year to filter on, e.g. 2023.",
            "type": "integer"
          }
        },
        "type": "object"
      },
      "name": "papers"
    },
    {
      "description": "What changed in the last hours or days — the escape hatch for facts newer than your training cutoff. Reach for this whenever the answer could have moved since you were trained: elections, appointments, acquisitions, releases, deaths, records. Returns titles with timestamps and edit comments; resolve any of them with lookup(). Pass `topic` to filter and `hours` to widen the window up to a week.",
      "inputSchema": {
        "properties": {
          "hours": {
            "description": "Look-back window in hours, 1-168 (default 24).",
            "type": "integer"
          },
          "limit": {
            "description": "Maximum changes, 1-100 (default 25).",
            "type": "integer"
          },
          "topic": {
            "description": "Optional case-insensitive filter on title or edit comment.",
            "type": "string"
          }
        },
        "type": "object"
      },
      "name": "recent"
    },
    {
      "description": "Find the right title when you only have a partial name or a rough description. Returns ranked {title, wikidata_id, description, summary_snippet}; ranking blends text relevance with monthly pageviews and follows redirects, so abbreviations land on the real article — \"usa\" returns United States, \"jfk\" returns John F. Kennedy, \"apple\" returns Apple Inc. rather than a disambiguation page. Searches every corpus at once unless you pass `corpus`. Follow up with lookup() for facts or article() for the text.",
      "inputSchema": {
        "properties": {
          "corpus": {
            "description": "Restrict to one corpus. Omit to search all of them at once, which is usually what you want when you do not know where the answer is.",
            "enum": [
              "wikipedia",
              "wiktionary",
              "wikiquote",
              "wikibooks",
              "wikivoyage",
              "wikiversity"
            ],
            "type": "string"
          },
          "limit": {
            "description": "Maximum results, 1-50 (default 10).",
            "type": "integer"
          },
          "query": {
            "description": "Free-text search query.",
            "type": "string"
          }
        },
        "required": [
          "query"
        ],
        "type": "object"
      },
      "name": "search"
    }
  ]
}

What the leaf hashes

{"body":"1846ffcfa1e8679d9255e7e1cde731238221805541af4b25c3543ad109fa7679","id":563353,"kind":"mcp.tools","method":"probe.mcp/1","observed_us":1793137690693257,"prev":null,"subject":"5f367ac3-cdbf-4c98-b535-6f8e2d407418","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. 0x9ef36735e6880c8b400211dcd49f365163db5b4abc300d5ca9659ae6bbbb6386
2. 0x01a5cc768e74e94bb0c31e9fe8d73bd3776d464a5207b4a6fab0e61a3b8db140
3. 0x796a4126e1d35215f2b054876f21a41e73cfe75176ad7d7f3812de1748d54b71
4. 0xef073db53240fca6567e0ddbd5175133a31719e4effc194016598fb7edc0cb76
5. 0x0d7831df688d9c43ae878144bf5d7fc81a88b163b4cfa3488ae4432f501a9994
6. 0xefc9e2ab2d28af964c07ed667a3c70ea8e6305dc945ac2513bfb546e97150016
7. 0xf6ef227f941e7d11cdb8bcc92ab0e9fdaf569f406c826026b12b8fd9a06d9f60
8. 0x0b206ddc9d21dfebe9d7a3744537d7e00f73095d658c8a2dabc9ed9b6c3b4af6
9. 0xabef868c46e77d0d9966ec3245f02388fcf2d26e306c93a2ca1198db8a15c4b5
10. 0xe9cfd0cd472cdf5a2f773d67db79839c98da33f88beb22465c719a71c68b2a2d
11. 0x70dbf5e56b082ea372d9f6f322b44de4dc06b3985cb3c1eb26e7a0dab527ff28
12. 0x121ffc8b387c54931bc53056e106d9ada0e2f7c1d32ebc819c3306406c952295
13. 0xa91c23113464e2fc1e93d41e407b7c6c254bee2eac2f7a550e2f9a1275eef26a
14. 0xb37e6165108af3c1962db6b76953aee5ee86201747a07444ad2760bf2d5dca23
15. 0xa3fff4662cd48659f5bafd0612931759a90e53a7b4209ed8f26f6594a07bb547
16. 0x64c405a28e8819716221eb2529fe4ce277ab08025e17e685cbd5ece75ceb75c7
17. 0xf30fddbf4811226c58e8f0daf36fddd79fc5f1356b592bb1d99a8061ddbabe66
18. 0x2bf8151e5f157dd183b9de6fa00469551932a7a6e3f33a694895000ac9061197
19. 0x9fc354c0ea3efc3f1b6548bb77ea902a50f2c9647647ea61592efce2bd795699

Day root

0xdd77e5e99da32c3f154180d63e3c3e6fe11e896a2c6896412ef6b5c4bcf7e795

Chained

0x23bb4dfd09f55c15ac3962f53578e02c6114d5bfd9bb451cf8794fb6e2817ef4

Published

On Arc Testnet, block 69414147: 0xb2ebd83aafeb765a0ce66e76aefb133ed06606c98038fd56191e2bcd1dcb77b4