Evidence row 508379

What was observed

{
  "count": 9,
  "tools": [
    {
      "description": "Answer a question from the corpus, or refuse. Returns only the claims that bear on the question, each with the sources it cites and its editorial confidence. When the corpus cannot answer, answered is false and abstention_reason plus missing_topics say what was not covered — a refusal is a real result here, not an error. Use this when the user asked a question in words; use search when you want to see the candidates yourself.",
      "inputSchema": {
        "$schema": "https://json-schema.org/draft/2020-12/schema",
        "properties": {
          "question": {
            "description": "The question to answer, in the words the user asked it.",
            "maxLength": 300,
            "minLength": 3,
            "type": "string"
          }
        },
        "required": [
          "question"
        ],
        "type": "object"
      },
      "name": "answer"
    },
    {
      "description": "Two to six knowledge objects side by side: their cards, every indexed attribute as a matrix (the same fields api/index.json publishes, null where an object does not say), the tags they share, the relations that run between them, the neighbours they have in common, and each one's claims with sources and confidence. Use this when the question is 'X vs Y', 'is X the same as Y' or 'how does X differ from Y' — search or get_topic first if you only have names, then pass the ids. An unknown id lands in `missing` with a near-miss suggestion and the rest are still compared.",
      "inputSchema": {
        "$schema": "https://json-schema.org/draft/2020-12/schema",
        "properties": {
          "ids": {
            "description": "Two to six knowledge object ids, as returned by search, get_topic or get_related, in the order you want the columns.",
            "items": {
              "maxLength": 100,
              "minLength": 1,
              "type": "string"
            },
            "maxItems": 6,
            "minItems": 2,
            "type": "array"
          }
        },
        "required": [
          "ids"
        ],
        "type": "object"
      },
      "name": "compare"
    },
    {
      "description": "Fetch one knowledge object by id, with its claims and the sources each claim cites. Use this once search, answer or get_topic has given you an id. An unknown id is not a dead end: the answer names near-miss ids you can retry with.",
      "inputSchema": {
        "$schema": "https://json-schema.org/draft/2020-12/schema",
        "properties": {
          "id": {
            "description": "The knowledge object's id, as returned by search, answer, get_topic or get_related — the last segment of its canonical URL.",
            "maxLength": 100,
            "minLength": 1,
            "type": "string"
          }
        },
        "required": [
          "id"
        ],
        "type": "object"
      },
      "name": "get_entity"
    },
    {
      "description": "Most recently verified knowledge objects (freshness signal). Use this to judge how current the corpus is, or to see what changed since you last read it. It ranks by verification date and ignores your topic entirely — use search or get_topic when you want objects that are relevant rather than recent.",
      "inputSchema": {
        "$schema": "https://json-schema.org/draft/2020-12/schema",
        "properties": {
          "limit": {
            "default": 10,
            "description": "How many recently verified objects to return, newest verification first.",
            "maximum": 25,
            "minimum": 1,
            "type": "integer"
          }
        },
        "type": "object"
      },
      "name": "get_latest"
    },
    {
      "description": "Corpus overview: what this instance knows, counts by type, published tags, freshness. Use this first when you land here and do not yet know whether this corpus can answer your question.",
      "inputSchema": {
        "$schema": "https://json-schema.org/draft/2020-12/schema",
        "properties": {},
        "type": "object"
      },
      "name": "get_overview"
    },
    {
      "description": "Graph neighbours of an object: outgoing and incoming relations, each with its relation type. PAGED: 25 relations by default, 200 at most, and a response budget of about 64 KB per call — a page over budget comes back shorter WITH a next_cursor, never truncated. One cursor walks both directions, outgoing first; its absence means you have the last page. Use this after get_entity to widen an answer with adjacent objects. It walks one hop from an id you already have — use search when you have a question and no starting object.",
      "inputSchema": {
        "$schema": "https://json-schema.org/draft/2020-12/schema",
        "properties": {
          "cursor": {
            "description": "Continue from a previous call: pass the next_cursor it returned. Opaque; do not construct one.",
            "maxLength": 200,
            "minLength": 1,
            "type": "string"
          },
          "id": {
            "description": "The knowledge object to walk out from, by id.",
            "maxLength": 100,
            "minLength": 1,
            "type": "string"
          },
          "limit": {
            "description": "How many relations to return, across both directions. Defaults to 25.",
            "maximum": 200,
            "minimum": 1,
            "type": "integer"
          }
        },
        "required": [
          "id"
        ],
        "type": "object"
      },
      "name": "get_related"
    },
    {
      "description": "The instance's source registry — each entry with its evidence tier, reliability and access date. PAGED: 25 entries by default, 200 at most, and a response budget of about 64 KB per call — a page over budget comes back shorter WITH a next_cursor, never truncated. Read next_cursor and call again to continue; its absence means you have the last page. Filter by object_id, tier, host or accessed_since to avoid paging through a registry you do not need. Use this when you need the registry entry behind a citation, or a sample of the whole registry to judge the corpus before trusting it; get_entity already tells you which sources a claim cites.",
      "inputSchema": {
        "$schema": "https://json-schema.org/draft/2020-12/schema",
        "properties": {
          "accessed_since": {
            "description": "Only sources accessed on or after this ISO date (YYYY-MM-DD).",
            "maxLength": 10,
            "minLength": 4,
            "type": "string"
          },
          "cursor": {
            "description": "Continue from a previous call: pass the next_cursor it returned. Opaque; do not construct one.",
            "maxLength": 200,
            "minLength": 1,
            "type": "string"
          },
          "host": {
            "description": "Only sources whose URL is served by this host, compared without a leading www. A source with no URL never matches.",
            "maxLength": 120,
            "minLength": 1,
            "type": "string"
          },
          "limit": {
            "description": "How many entries to return. Defaults to 25.",
            "maximum": 200,
            "minimum": 1,
            "type": "integer"
          },
          "object_id": {
            "description": "Restrict the answer to the sources cited by this knowledge object. Omit it to page the whole registry.",
            "maxLength": 100,
            "minLength": 1,
            "type": "string"
          },
          "tier": {
            "description": "Only sources of this evidence tier, spelled exactly as get_sources reports it.",
            "maxLength": 60,
            "minLength": 1,
            "type": "string"
          }
        },
        "type": "object"
      },
      "name": "get_sources"
    },
    {
      "description": "List the knowledge objects carrying a tag (topics are content-backed tags). PAGED: 25 objects by default, 200 at most, and a response budget of about 64 KB per call — a page over budget comes back shorter WITH a next_cursor, never truncated. Read next_cursor and call again to continue, and its absence means you have the last page. Use this to browse a known topic; use search when you have a question rather than a tag, and get_overview to see which tags exist. An unknown tag comes back with the topics that do exist, so a miss still moves you forward.",
      "inputSchema": {
        "$schema": "https://json-schema.org/draft/2020-12/schema",
        "properties": {
          "cursor": {
            "description": "Continue from a previous call: pass the next_cursor it returned. Opaque; do not construct one.",
            "maxLength": 200,
            "minLength": 1,
            "type": "string"
          },
          "limit": {
            "description": "How many objects to return. Defaults to 25.",
            "maximum": 200,
            "minimum": 1,
            "type": "integer"
          },
          "tag": {
            "description": "The topic to list, spelled exactly as get_overview publishes it. Every published topic has objects under it, so an empty answer means the topic does not exist.",
            "maxLength": 100,
            "minLength": 1,
            "type": "string"
          }
        },
        "required": [
          "tag"
        ],
        "type": "object"
      },
      "name": "get_topic"
    },
    {
      "description": "Full-text search over the knowledge graph. Matching ignores accents and apostrophes, so query in the user's own words; every hit carries the fields it matched and a score. BM25 relevance: each query term is weighted by how RARE it is in this corpus and by where it hits (name 3, tags 2, questions 2, body 1). A hit must also cover a minimum share of the question's information, measured in the same rarity weights — matching only common words does not qualify. Centrality (how many objects point at this one) breaks TIES ONLY and is never part of the score, so it cannot make an irrelevant object rank. Two hits with the same matched_fields can still differ: the score is rarity-weighted, so matching a rare term is worth more than matching a common one. Use this whenever you have a question rather than an id, then follow up with get_entity.",
      "inputSchema": {
        "$schema": "https://json-schema.org/draft/2020-12/schema",
        "properties": {
          "limit": {
            "default": 10,
            "description": "How many ranked hits to return, best first.",
            "maximum": 25,
            "minimum": 1,
            "type": "integer"
          },
          "query": {
            "description": "What to look for, in the user's own words. Matched against titles, questions, claim text and tags; accents and apostrophes are ignored, so 'jose' finds 'José'.",
            "maxLength": 200,
            "minLength": 1,
            "type": "string"
          }
        },
        "required": [
          "query"
        ],
        "type": "object"
      },
      "name": "search"
    }
  ]
}

What the leaf hashes

{"body":"775eca08995cc45541bd59702ab7fec1cd1f55f7e7854fe037f9b8f81dbcd4c1","id":508379,"kind":"mcp.tools","method":"probe.mcp/1","observed_us":1793116385848490,"prev":null,"subject":"1459b928-476c-43ff-ab5f-ee25e4b50a49","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. 0xaffef0d67a1d8a697b9889e170517f2a2bf87991543d532889227e3db92c87a5
2. 0xbf08323638c662a7c169ef275f10af84eccfc3b07fda0dda2e4f178ad0a8716d
3. 0x8a77fa64f5315c7a791a7c38f041fadd3371bd4e30631d349fc63e774a036ce0
4. 0x0238a2e2bd5b814d0b0cff256bce0551fc8fd719a902796c6b8ab247b50768c6
5. 0x45e98864be5103a35f21c52670e3c302ce9234681c513db6f93300ce2a8476f9
6. 0x04c9f34ff2663d34c213e070078cd718f6292aeebc2295598e170f3a9d069079
7. 0x863d0328b12aeabd956adc791a55a5f0a8a1c2fd078b3347be8963d8fb302fb7
8. 0x4889dcd7f9bfe062d4e81804beec7f8742a744b45de5699e7f845eb65cfa4306
9. 0x21a4d48f38ca7c0d6cd6401e0c696d9404c0ab6ad6ce1d356c20be74a42c0233
10. 0xd0b754d6a919ae0618fb43b587cf99ce18534aaf6627ab545862bc00e6bb323d
11. 0xb401429c8bd10413f6592bff0d0ca36d15f4057fe715a18ac4410a9b8e3c3342
12. 0x6413c10d59083704c26ae4eedf79ca24dbf9c92cad846aac6f2a11f02ab3b17b
13. 0x4c82d20074b9d7d77e728b0250a2c662412d8d0f7eccff2d8100bfcc4434597f
14. 0x98c6ed3e7b5bb7f1757a8660af410226a11dd563bf83010040c2ce059ab45e54
15. 0xec7bb68216a4deb1960acfbbd75da88f7e468b58adb9cf3ec62f2790e46508b3
16. 0x64c405a28e8819716221eb2529fe4ce277ab08025e17e685cbd5ece75ceb75c7
17. 0xf30fddbf4811226c58e8f0daf36fddd79fc5f1356b592bb1d99a8061ddbabe66
18. 0x2bf8151e5f157dd183b9de6fa00469551932a7a6e3f33a694895000ac9061197
19. 0x9fc354c0ea3efc3f1b6548bb77ea902a50f2c9647647ea61592efce2bd795699

Day root

0xdd77e5e99da32c3f154180d63e3c3e6fe11e896a2c6896412ef6b5c4bcf7e795

Chained

0x23bb4dfd09f55c15ac3962f53578e02c6114d5bfd9bb451cf8794fb6e2817ef4

Published

On Arc Testnet, block 69414147: 0xb2ebd83aafeb765a0ce66e76aefb133ed06606c98038fd56191e2bcd1dcb77b4