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Evidence row 299245

Observed , by probe.mcp/1.

Kind
mcp.tools
About
endpoint b65a65bc-cf9f-4c9e-a9ba-628810512d8f
Recorded
Previous row
None: the first of its kind about this subject
Body SHA-256
0c9e5b94badec74e69189d8b2574ed7e17470a29f38571b3e0b5394f70709940
Leaf
0xd7d2f69a6f013e4307ec0926d852163a44b6bed1a3aa6c441769b74a178ade72

What was observed

{
  "count": 1,
  "tools": [
    {
      "description": "Estimate a causal effect from a dataset and answer a business question.\n\nProvide exactly one of ``csv`` (CSV text) or ``data`` (list of row objects).\nReturns the full long-form Pulse report (the same answer an email reply\ncontains) as the primary text content and as ``report`` in the structured\nresult, followed by chart PNGs as image content blocks (unless\n``include_charts`` is false). Structured fields: the adjusted effect size\n(``ate``/``effect_size``) with its confidence\ninterval (``ci_low``, ``ci_high``, ``ci_range``), per-channel effects\n(``channels``, keyed by outcome), confidence, and caveats. A ``test_design``\n(how long a spend-cut test would take to confirm the effect, and whether the data\nalready suffices) is included only when the question asks about sample size,\npower, or how long a test would take, or when ``include_test_design`` is true;\notherwise it is null. With ``include_guardian_verdict`` it also returns\n``guardian_verdict`` ``{verdict: pass|caution|hold, reason}``, computed locally.\nAll numbers are deterministic statistics, never LLM-generated.\n\nArgs:\n    question: The business question, e.g. \"Does ad spend drive sales?\".\n    csv: Dataset as CSV text.\n    data: Dataset as a list of row records (alternative to ``csv``).\n    cause: Treatment column (inferred from the question if omitted).\n    effect: Outcome column (inferred if omitted).\n    confounders: Columns to adjust for, in addition to those discovered.\n    outcomes: Several outcome columns, for by-channel effects.\n    time_col: Time column (used for the test-design cadence).\n    include_guardian_verdict: Also return a pass/caution/hold verdict.\n    include_charts: Attach chart images (DAG, effect, trend) as image content.\n    include_test_design: Force the spend-cut test design / sample-size section on\n        (true) or off (false). Omit (null) to include it only when the question\n        asks about sample size, statistical power, or how long a test would take.\n",
      "inputSchema": {
        "properties": {
          "cause": {
            "anyOf": [
              {
                "type": "string"
              },
              {
                "type": "null"
              }
            ],
            "default": null,
            "title": "Cause"
          },
          "confounders": {
            "anyOf": [
              {
                "items": {
                  "type": "string"
                },
                "type": "array"
              },
              {
                "type": "null"
              }
            ],
            "default": null,
            "title": "Confounders"
          },
          "csv": {
            "anyOf": [
              {
                "type": "string"
              },
              {
                "type": "null"
              }
            ],
            "default": null,
            "title": "Csv"
          },
          "data": {
            "anyOf": [
              {
                "items": {
                  "additionalProperties": true,
                  "type": "object"
                },
                "type": "array"
              },
              {
                "type": "null"
              }
            ],
            "default": null,
            "title": "Data"
          },
          "effect": {
            "anyOf": [
              {
                "type": "string"
              },
              {
                "type": "null"
              }
            ],
            "default": null,
            "title": "Effect"
          },
          "include_charts": {
            "default": true,
            "title": "Include Charts",
            "type": "boolean"
          },
          "include_guardian_verdict": {
            "default": false,
            "title": "Include Guardian Verdict",
            "type": "boolean"
          },
          "include_test_design": {
            "anyOf": [
              {
                "type": "boolean"
              },
              {
                "type": "null"
              }
            ],
            "default": null,
            "title": "Include Test Design"
          },
          "outcomes": {
            "anyOf": [
              {
                "items": {
                  "type": "string"
                },
                "type": "array"
              },
              {
                "type": "null"
              }
            ],
            "default": null,
            "title": "Outcomes"
          },
          "question": {
            "title": "Question",
            "type": "string"
          },
          "time_col": {
            "anyOf": [
              {
                "type": "string"
              },
              {
                "type": "null"
              }
            ],
            "default": null,
            "title": "Time Col"
          }
        },
        "required": [
          "question"
        ],
        "title": "pulse_analyzeArguments",
        "type": "object"
      },
      "name": "pulse_analyze"
    }
  ]
}

What the leaf hashes

{"body":"0c9e5b94badec74e69189d8b2574ed7e17470a29f38571b3e0b5394f70709940","id":299245,"kind":"mcp.tools","method":"probe.mcp/1","observed_us":1793052675870846,"prev":null,"subject":"b65a65bc-cf9f-4c9e-a9ba-628810512d8f","subject_kind":"endpoint"}

Proof

Fold these into the leaf in order, hashing each sorted pair with keccak256, to reach the root of (296695 rows).

  1. 0xd7d30ccb66c61513b5b24dfaa1b2847dc6549cfb5d051dfff56846d7d9f9348a
  2. 0x49fbc87fc4546ec023e9c216617a52bb815a65bbffb354621fb76a1d30af66ab
  3. 0x393d7293a2bc5f8add479f428b3d37d582d3e8d532dad144adbb30c0b2ddfb71
  4. 0xf50cf268c073cdbe9d4c17a2db68f53730011c7921192fbefbc1c801b0852fa9
  5. 0x46e0f74fd261d16b12e1812234d43aba1572cc3da5bb457b8a249f6ab70eb8a6
  6. 0xa4901d6d751b0678b5c1d1f962c4f202776bf745ac85c4360b7c9d9c9c28262c
  7. 0x20e7d9243d1b30a5b3e22533c7d1c118c07082fbe4e7e1f93d5582aac3557a37
  8. 0x491ffb22b6faa4f27b3abcc2471784d7fa0064d960525b5463a4ef5fb68217a3
  9. 0x421ef9a649a8881c0ca743e8fda5d3c2a2f63232806f1fbb0768307d8cca1f0b
  10. 0x99d243e75489137f42f22b35cac536858bfa54cac8df9dedbb78852b9f686a91
  11. 0x2671a86f48dece064e871ca46396549de6c07bd09915bed27b7ac95a735127fd
  12. 0x07e91966e8a0c43a0725bd3d523259064a93333c2900e6171b69ffbfd9f423f1
  13. 0xcd9e3af1fa905ec61b99ee9442716d7c7a439aecacd018ec229a24def1cabf6b
  14. 0x6b927d53caa5125cdba9255612d524976c691f8672796c3f0885ba2c5b3af1ad
  15. 0x6dc40f983a3840a0ad927a9ca17f4ff1e3b649a6c735e2a09b140db7c2caf13b
  16. 0x703f95866af09c5e5d6ead7411e9a10f964a41a2aff64d4a55ff0053c023a023
  17. 0xd0651cb9bc8155eb52b5247d506e74b8a935057dc190ba52091702ac25ed3a40
  18. 0x9a9024edb5069d88a324facb310a5ce8e495214bbaa968f077bede437390194f
  19. 0x9b9ce5b74b9bab5121612ef6b64920a8d453cbf311096a423eef9702747e868a
Day root
0xf43e8dcc3d451a4788b26be904c55e8c4c0fa75c9666796c51db38d37e2c0df8
Chained
0x702d5ac508f08b0d1accf36af25822d8e5660367494edd17a642fb61e158a57a
Published
On Arc Testnet, block 69251483: 0x495162f8936e19f0039918feebe6c307f327cd6bf8c37219620b1970041fa9ae

How to check it · This row as JSON