Evidence row 562812

What was observed

{
  "count": 8,
  "tools": [
    {
      "description": "Hot, warm, and paused storage tiers: current per-GB prices with what each tier means for query latency and when to pick it.",
      "inputSchema": {
        "$schema": "http://json-schema.org/draft-07/schema#",
        "properties": {},
        "type": "object"
      },
      "name": "compare_storage_tiers"
    },
    {
      "description": "List public REST API operations, or pass one (e.g. \"POST /api/v1/query\") for its parameters, request body, responses, and auth. Sourced from the published OpenAPI spec.",
      "inputSchema": {
        "$schema": "http://json-schema.org/draft-07/schema#",
        "additionalProperties": false,
        "properties": {
          "operation": {
            "description": "Operation key like \"POST /api/v1/query\"; omit to list all operations",
            "type": "string"
          }
        },
        "type": "object"
      },
      "name": "get_api_operation"
    },
    {
      "description": "Measured benchmark results and full reproduction methodology (VectorDBBench through the public API), including dataset, recall, and cost.",
      "inputSchema": {
        "$schema": "http://json-schema.org/draft-07/schema#",
        "properties": {},
        "type": "object"
      },
      "name": "get_benchmark_methodology"
    },
    {
      "description": "Current storage-tier pricing, monthly usage credits, and billing cycle. Storage-only pricing: no per-query or egress fees.",
      "inputSchema": {
        "$schema": "http://json-schema.org/draft-07/schema#",
        "properties": {},
        "type": "object"
      },
      "name": "get_pricing"
    },
    {
      "description": "The Vector Panda quickstart: install the veep Python SDK, upload a file, and run similarity queries end to end.",
      "inputSchema": {
        "$schema": "http://json-schema.org/draft-07/schema#",
        "properties": {},
        "type": "object"
      },
      "name": "get_quickstart"
    },
    {
      "description": "File formats accepted for vector ingestion, from the canonical features page.",
      "inputSchema": {
        "$schema": "http://json-schema.org/draft-07/schema#",
        "properties": {},
        "type": "object"
      },
      "name": "list_supported_formats"
    },
    {
      "description": "Fetch the full Markdown of one document by its site path (for example /docs/concepts/embeddings-101), as returned by search_docs.",
      "inputSchema": {
        "$schema": "http://json-schema.org/draft-07/schema#",
        "additionalProperties": false,
        "properties": {
          "path": {
            "description": "Site path of the document, e.g. /docs/tutorials/filter-syntax",
            "type": "string"
          }
        },
        "required": [
          "path"
        ],
        "type": "object"
      },
      "name": "read_doc"
    },
    {
      "description": "Full-text search across Vector Panda concepts, tutorials, projects, and the engineering build log. Returns ranked matches with canonical URLs and freshness.",
      "inputSchema": {
        "$schema": "http://json-schema.org/draft-07/schema#",
        "additionalProperties": false,
        "properties": {
          "limit": {
            "description": "Maximum results (default 5, cap 20)",
            "maximum": 20,
            "minimum": 1,
            "type": "integer"
          },
          "query": {
            "description": "Search terms",
            "type": "string"
          }
        },
        "required": [
          "query"
        ],
        "type": "object"
      },
      "name": "search_docs"
    }
  ]
}

What the leaf hashes

{"body":"b4bb8fdb01821ae985530b02591b3a0a1453fda601c5efffb33140f42ad4a413","id":562812,"kind":"mcp.tools","method":"probe.mcp/1","observed_us":1793137517775164,"prev":null,"subject":"219e59d8-5f20-450d-a169-c28aecd67b1c","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. 0x26212abbfe0018e4b6a41f40cb735d8c25f4bc6e7f03e4d655f22686671028c0
2. 0xeba54592173661b66ccd4a27a98a3d2840d3edaa10429ea40d4834d8a16abd7b
3. 0xb294be0f2967fefc9b10bc333d3aaded7d8797cb901cebf14ee74e96cd666779
4. 0x89a052065223465ca4d20490808e9f593b77628c999ddcd0bdd02642cac0a4be
5. 0x58ac018a43da620830452ce19365ca792cd14f09a652df9acd6a54b5d3b9a86d
6. 0x653fb3d5e5a544c3c0b938a30ca9bc1900202a37248bf5671edd857a3ef561ec
7. 0x16fbefcd669cb95a9e98bb53cd28a94417a999da3d3695768b7e4ab4ff6f038d
8. 0x14e896760099b153a6a06c1f4d07b4d921b90cc2945dd1cddfb13930faf5d3ab
9. 0xa39277d7ecd94686f289401abbda508684234f2a4bd405e26ae74a1cb3407654
10. 0xd2b46af5d576579f388a5f8e3d4ee40ea1d1a94cdd99f416479167bfe3be5f5a
11. 0xb74d1e1417c2953e5b3c2385126d77946e4effdefe4584289b6f7f7b0a99d30c
12. 0x442ef05dbe7931d22c60e72d459fdb2874e7d0eab89c16f41f0a865bf55610d6
13. 0x1a710d4f8e6092d584b581d8599ff9183c0008f9275cf0601c925b43b4e84ae6
14. 0xe8ee6f6ed50253c5ee2d1aa85a4b1a38ce94bca7f3b72fd1850f496152dbe285
15. 0xa383a1cfe9b3f6af67eb4c7642564b9424d46d7fca90e7ba175d549f8b67dd0c
16. 0x52efa60e0c3d8525486d2310eb3b6b566c9677c64fd1f819edb072cf101bcbdb
17. 0x22c1fb9487ee0b9732fa017a0cc321eb27a60c08450d112f755089796e12cb1c
18. 0xa20d48798a34aba12eb9c7297722982d910f06c148f5e27ac59307422964a585
19. 0x9fc354c0ea3efc3f1b6548bb77ea902a50f2c9647647ea61592efce2bd795699

Day root

0xdd77e5e99da32c3f154180d63e3c3e6fe11e896a2c6896412ef6b5c4bcf7e795

Chained

0x23bb4dfd09f55c15ac3962f53578e02c6114d5bfd9bb451cf8794fb6e2817ef4

Published

On Arc Testnet, block 69414147: 0xb2ebd83aafeb765a0ce66e76aefb133ed06606c98038fd56191e2bcd1dcb77b4