Evidence row 564873

What was observed

{
  "count": 6,
  "tools": [
    {
      "description": "Which tracked models run on a given GPU or Mac: measured GGUF weights + computed context cache + runtime overhead versus usable memory. Returns the best recommendation and every verdict (EXCELLENT/GOOD/TIGHT/OFFLOAD_REQUIRED/NOT_RECOMMENDED/UNKNOWN) with plain-language reasons. Get hardware ids from list_hardware.",
      "inputSchema": {
        "$schema": "https://json-schema.org/draft/2020-12/schema",
        "properties": {
          "context": {
            "description": "Context length in tokens (default 8192).",
            "maximum": 1048576,
            "minimum": 512,
            "type": "integer"
          },
          "hardware": {
            "description": "Hardware id or page slug, e.g. rtx-4090, mac-studio-m3-ultra-96gb.",
            "type": "string"
          },
          "kv": {
            "description": "KV-cache quantization (default f16).",
            "enum": [
              "f16",
              "q8_0",
              "q4_0"
            ],
            "type": "string"
          },
          "model": {
            "description": "Restrict to one model slug.",
            "type": "string"
          }
        },
        "required": [
          "hardware"
        ],
        "type": "object"
      },
      "name": "find_fit"
    },
    {
      "description": "Median verified on-demand rental price per GPU class on Vast.ai (USD per hour), with min/p75 and offer counts, the collection timestamp, and per class the Rent Index: this week's median against last week and against the first week collected, a trend word, and the days excluded as marketplace glitches, plus RunPod's lowest posted on-demand price per class (a list price, not a median), Clore.ai's median for the same class (a second marketplace, never blended), and the AWS, Azure and Oracle Cloud pay-as-you-go list prices per GPU-hour, each with the instance type, VM size or bare-metal shape the price sits in (list prices, read four times a day, no statement about capacity). The index describes what prices did; it never forecasts.",
      "inputSchema": {
        "$schema": "https://json-schema.org/draft/2020-12/schema",
        "properties": {},
        "type": "object"
      },
      "name": "gpu_prices"
    },
    {
      "description": "Curated GPU and Mac profiles the fit engine knows — ids, memory, usable memory after margins, bandwidth. Use an id with find_fit.",
      "inputSchema": {
        "$schema": "https://json-schema.org/draft/2020-12/schema",
        "properties": {},
        "type": "object"
      },
      "name": "list_hardware"
    },
    {
      "description": "Daily price history per GPU class and source, up to 90 days: the rows the Rent Index and the 30-day range are computed from. Marketplace days (Vast.ai, Clore.ai) are medians over the day's counted hourly medians with min, p75 and offer count; list-price days (RunPod, AWS, Azure, Oracle Cloud, Lambda, Nebius, CoreWeave, Hyperstack) carry the posted price. Each row says which kind it is; never blend the two. Describes what prices did; it never forecasts.",
      "inputSchema": {
        "$schema": "https://json-schema.org/draft/2020-12/schema",
        "properties": {
          "days": {
            "description": "Days back from today (default 30, max 90).",
            "maximum": 90,
            "minimum": 1,
            "type": "integer"
          },
          "gpu": {
            "description": "One class by slug or name, e.g. rtx-4090, h100. Every class without it.",
            "type": "string"
          },
          "source": {
            "description": "One source key, e.g. vast, runpod, aws. Every published source without it.",
            "type": "string"
          }
        },
        "type": "object"
      },
      "name": "price_history"
    },
    {
      "description": "Tracked AI models ranked by Heat Score (0–100, weighted percentiles of measured Hugging Face/OpenRouter signals) with the raw signals, local-run facts (GGUF size, quantization) and links. Models still collecting a week of history have heat=null and rank after scored ones.",
      "inputSchema": {
        "$schema": "https://json-schema.org/draft/2020-12/schema",
        "properties": {
          "limit": {
            "description": "How many models to return (default 12).",
            "maximum": 100,
            "minimum": 1,
            "type": "integer"
          },
          "slug": {
            "description": "Return a single model by slug.",
            "type": "string"
          }
        },
        "type": "object"
      },
      "name": "trending_models"
    },
    {
      "description": "The current pick of the week: one tracked model chosen by a published rule (largest counted Heat Score rise among models that run comfortably on a consumer card of up to 24 GB), with the numbers frozen at selection time, a device-by-device fit ladder and the written report including its caveats. Pass week (e.g. 2026-w37) for a past issue. issue is null until the first issue is published.",
      "inputSchema": {
        "$schema": "https://json-schema.org/draft/2020-12/schema",
        "properties": {
          "week": {
            "description": "ISO week label of a past issue, e.g. 2026-w37 (default: the current issue).",
            "pattern": "^\\d{4}-w\\d{2}$",
            "type": "string"
          }
        },
        "type": "object"
      },
      "name": "weekly_pick"
    }
  ]
}

What the leaf hashes

{"body":"3983dc85597e05542a494bc8295ea87211ae1a6aeb877b861f6bd926a2c932b1","id":564873,"kind":"mcp.tools","method":"probe.mcp/1","observed_us":1793137957960194,"prev":null,"subject":"25c66ffe-c1e8-4726-8e78-6cf4d2185008","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. 0x7a54c6045c697d84e371691602291cd91f78fef742e108f556161905c5b979ba
2. 0xb5cd612d9059038fa5c0e51ed569c203a869b3e360709ba1c7e460da433ab643
3. 0x6c8f676493564bb1d337917a33df20bac855cd54dd4e6375da03b02bd57474ba
4. 0xa558ad2b274306ef482fc290a51c2d8f93be8939df89cbffb4b4394e6e365564
5. 0xfa0aade6c546babceef792b5799047fa15d4793b8893df5e62b4e30702017e00
6. 0x3895d8db0a4ef3d87f6f398041b844dc8f9d733eb720fb4eaef6d4085f6e93f3
7. 0xf764f2b585a5d0c1ccef11e1efe99d4bf79a55ecd4731100d7a92b4aa640bd48
8. 0x2c401fd6856ae7f734aca9b3154123dd5b2b1d09e830bd740019e1f7bce340d1
9. 0xa2cab213aa2d823e9f94de7f447b2a92773478aeffbcd1323368337fb0c57ce6
10. 0xe9a5d1c6719d67a94924cfd78ac5d857dfe591b756d7a88a4f7a4692f2e58d71
11. 0x926531ee7b85e18ef250d5e2cf7f1fe039b14d7d9a1ebadd7e87f963de66741a
12. 0x0574b16d30fa9d3621e88c3779ea2cd142521826d4e75735f3ae6d4ed075cfc4
13. 0x8f2d125f5fe55a24b5da16577766eeaa13f24a272bb4c1c016f423ae917da681
14. 0x9848c068b1371232529d2b5738fdc45b854c57da041b69ea7c9e112b14e2d9d1
15. 0x275720ecabb68df33eee92ae4fd70572a8d952d6c61c061c2869cfc3217aa5c5
16. 0x23cf9bb4337ee8be7fcae368fd69d24c62c11fb45882127b4cd919bb96a19996
17. 0x0e01aa9525097570a6edaf32bfc959de57bf5c24317cf10c0716affea85ca60d
18. 0xa20d48798a34aba12eb9c7297722982d910f06c148f5e27ac59307422964a585
19. 0x9fc354c0ea3efc3f1b6548bb77ea902a50f2c9647647ea61592efce2bd795699

Day root

0xdd77e5e99da32c3f154180d63e3c3e6fe11e896a2c6896412ef6b5c4bcf7e795

Chained

0x23bb4dfd09f55c15ac3962f53578e02c6114d5bfd9bb451cf8794fb6e2817ef4

Published

On Arc Testnet, block 69414147: 0xb2ebd83aafeb765a0ce66e76aefb133ed06606c98038fd56191e2bcd1dcb77b4