Evidence row 736786

What was observed

{
  "count": 11,
  "tools": [
    {
      "description": "Compare two or more of You.com, Tavily, Exa and Parallel side by side, overall or within one use case, optionally filtered to metrics whose name contains a keyword (e.g. 'latency', 'accuracy', 'fresh', 'cost', 'empty').",
      "inputSchema": {
        "$schema": "http://json-schema.org/draft-07/schema#",
        "properties": {
          "metric_filter": {
            "description": "Optional keyword to keep only matching metrics",
            "type": "string"
          },
          "providers": {
            "description": "Two or more of: You.com, Tavily, Exa, Parallel",
            "items": {
              "enum": [
                "You.com",
                "Tavily",
                "Exa",
                "Parallel"
              ],
              "type": "string"
            },
            "minItems": 2,
            "type": "array"
          },
          "use_case": {
            "default": "overall",
            "description": "overall, gtm, insurance or finance",
            "enum": [
              "overall",
              "gtm",
              "insurance",
              "finance"
            ],
            "type": "string"
          }
        },
        "required": [
          "providers"
        ],
        "type": "object"
      },
      "name": "compare_providers"
    },
    {
      "description": "Full text of one benchmark document by its ID (for example question-F5, scorecard-finance, overall, methodology).",
      "inputSchema": {
        "$schema": "http://json-schema.org/draft-07/schema#",
        "properties": {
          "id": {
            "description": "Document ID from search",
            "type": "string"
          }
        },
        "required": [
          "id"
        ],
        "type": "object"
      },
      "name": "fetch"
    },
    {
      "description": "The published graphs: overall scorecard, key metrics and per-query grids for GTM, insurance and finance, and developer activation. Without chart_id, lists them with titles and image links; with chart_id, also returns the image.",
      "inputSchema": {
        "$schema": "http://json-schema.org/draft-07/schema#",
        "properties": {
          "chart_id": {
            "description": "e.g. overall_scorecard, finance_key_metrics, gtm_query_grid, time_steps_activation",
            "type": "string"
          },
          "use_case": {
            "default": "all",
            "description": "Filter the list",
            "enum": [
              "all",
              "gtm",
              "insurance",
              "finance",
              "setup"
            ],
            "type": "string"
          }
        },
        "type": "object"
      },
      "name": "get_charts"
    },
    {
      "description": "How the benchmark was run and graded: query design, identical request parameters, 3 runs per query, latency timing, SimpleQA and FreshQA grading, the 10 metrics and the standards they follow, and limits.",
      "inputSchema": {
        "properties": {},
        "type": "object"
      },
      "name": "get_methodology"
    },
    {
      "description": "Headline findings and the overall scorecard across all 30 queries (90 calls per API): accuracy, fully correct, precision@5, freshness, empty responses, failed calls, latency p50/p95, cost per correct answer. Also the per-use-case summary, which API fits which agent, and developer activation time.",
      "inputSchema": {
        "properties": {},
        "type": "object"
      },
      "name": "get_overall_results"
    },
    {
      "description": "One benchmark question by ID (e.g. G3, I4, F5): the task, search call, parameters, answer key with sources, and what each API returned with its accuracy score (0-2, mean of 3 runs).",
      "inputSchema": {
        "$schema": "http://json-schema.org/draft-07/schema#",
        "properties": {
          "id": {
            "description": "Question ID, e.g. G3, I4 or F5",
            "type": "string"
          }
        },
        "required": [
          "id"
        ],
        "type": "object"
      },
      "name": "get_question"
    },
    {
      "description": "Full metric scorecard for one use case (gtm, insurance, finance) or 'overall'. Metrics: search-only latency p50/p95 (server and client), calls under 1 s, accuracy, freshness (% in window, newest relevant result age), content in one call, tokens, search cost.",
      "inputSchema": {
        "$schema": "http://json-schema.org/draft-07/schema#",
        "properties": {
          "use_case": {
            "description": "overall, gtm, insurance or finance",
            "enum": [
              "overall",
              "gtm",
              "insurance",
              "finance"
            ],
            "type": "string"
          }
        },
        "required": [
          "use_case"
        ],
        "type": "object"
      },
      "name": "get_scorecard"
    },
    {
      "description": "How to rerun this benchmark on your own machine with your own API keys, including on your own queries. Nothing runs on this server.",
      "inputSchema": {
        "properties": {},
        "type": "object"
      },
      "name": "how_to_run_locally"
    },
    {
      "description": "The 30 benchmark questions (G1-G10 GTM, I1-I10 insurance, F1-F10 finance): ID, what the agent is asked, the search call and freshness.",
      "inputSchema": {
        "$schema": "http://json-schema.org/draft-07/schema#",
        "properties": {
          "use_case": {
            "default": "all",
            "description": "all, gtm, insurance or finance",
            "enum": [
              "all",
              "gtm",
              "insurance",
              "finance"
            ],
            "type": "string"
          }
        },
        "type": "object"
      },
      "name": "list_questions"
    },
    {
      "description": "Which web search API fits an agent, based on this benchmark: live voice/chat turns, deep research, semantic or long-tail queries, docs lookups, or a specific use case (GTM, insurance, finance). Returns the guidance with the supporting numbers.",
      "inputSchema": {
        "$schema": "http://json-schema.org/draft-07/schema#",
        "properties": {
          "agent_need": {
            "description": "What the agent does, e.g. 'voice agent answering in under a second' or 'finance research'",
            "type": "string"
          }
        },
        "type": "object"
      },
      "name": "recommend_provider"
    },
    {
      "description": "Search the Web Search API Eval benchmark (results, questions, methodology, charts, how to run). Returns matching document IDs, titles and URLs.",
      "inputSchema": {
        "$schema": "http://json-schema.org/draft-07/schema#",
        "properties": {
          "query": {
            "description": "What to look for",
            "type": "string"
          }
        },
        "required": [
          "query"
        ],
        "type": "object"
      },
      "name": "search"
    }
  ]
}

What the leaf hashes

{"body":"b7d64ec3baf3b9b4191c523a56afa140d4b816b980fbcf3007b75f287e4c5523","id":736786,"kind":"mcp.tools","method":"probe.mcp/1","observed_us":1793158169484872,"prev":null,"subject":"02837a79-9872-477f-b9f2-11f49d640ec8","subject_kind":"endpoint"}

Proof

Fold these into the leaf in order, hashing each sorted pair with keccak256, to reach the root of 2026-10-28 (231336 rows).

1. 0x8cce84c84e76e409c521adb2e4ee37493ac9f19888c8e4cbc45ee8fce43077b4
2. 0x01000cbc0fe71d059446fd5e7cff48eea3f4eeeacabd515ab1fb2cc4cb9ddc66
3. 0x00db5138deb523045734c71a02b8b805ff4fb3040c1d875af09cfc417025917b
4. 0x30309772d20941ec13157bf13d1048b975d961e604c38bec69ef7a93d88e34c2
5. 0xc365158d5c586d37dc57c755381c832270c345e54bba138e42c088b3def67d2b
6. 0xf9ad6dfed470d3ade44c56f891c8164ec30d682ab5e6108356107906aa536e1a
7. 0xf0ae7cc07456d5caeff1c70f4ebfe2296104dd816015f3a70828280ebde1bc23
8. 0x238a4febd48aca99c2571499355550ae648ee3e49bd245c5a3d085436b9bf656
9. 0x42c1def2e8e02bf1dfd8860e1ee725dd7da4ad8d942449bd95b735cce4cd324a
10. 0x60b0d7a178f72fc3e21dac1cee2b9657b756f3f53b846cf9666ffff27ad3c163
11. 0x629019a302eb28956efe0ab26b14729dbeb5be95de1124d8372526c03892aeaf
12. 0x9560e0a6c078eef64083ffc1591f9aab803bac81e1815abba76b7711510818cb
13. 0xdbb215681a11fa9a73d4086f7e02f913b35a590ef20bcbcc46a343cbef08a9da
14. 0x115e196b75a0422533397227b51b9c807591ec2574919c03800bb0d253f110e8
15. 0xe2f9c120d14f36fdddf9b4e605c5d0b8a13b835e2380472255dc22e14b93e84e
16. 0x74cf00e07b52415c2184cf20be69d195ed2725497a6892c2f3e449497c17d0ec
17. 0x7a743a0a09057bae687051274e1d45bf973bd36a1a4ba16a191b5d29e3bdfa2a
18. 0xe027395fafb7fc41921c42ca0542524ea22204cbd5908b70b27f3d5fb26f5d0f

Day root

0x2c19037464cc4c493624565f01775d6446fc78eaa118fc6865bfe2bfca9ff78e

Chained

0xd1d7dee8322ceff8f4c8d83aa1fe4a4466825c2af1c97dbde3d35db61234f7be

Published

On Arc Testnet, block 69584042: 0x8bae2b1d097834c8b458513862d70d29ee807e98dc2e863b27250131c15b35ae