Evidence row 812650

What was observed

{
  "count": 5,
  "tools": [
    {
      "description": "No API key needed. Static-analyze a public GitHub repo (e.g. {\"repo\": \"owner/name\"}) or a set of source files for LLM token waste: uncached static prompts, frontier models on extraction tasks, hot loops, non-deterministic repeat calls, and prompt-caching mis-integration (caching gated on model names, volatile values in the system prompt, random ids in history, wrong-provider cache markers, misplaced breakpoints, cost math that ignores cached tokens). Returns findings with fixes and an estimated recoverable percentage.",
      "inputSchema": {
        "properties": {
          "files": {
            "description": "Alternative to repo: files to analyze",
            "items": {
              "properties": {
                "content": {
                  "type": "string"
                },
                "path": {
                  "type": "string"
                }
              },
              "required": [
                "path",
                "content"
              ],
              "type": "object"
            },
            "type": "array"
          },
          "repo": {
            "description": "GitHub owner/name (public)",
            "type": "string"
          }
        },
        "type": "object"
      },
      "name": "audit_repo_token_waste"
    },
    {
      "description": "No API key needed; only `model` is required (e.g. {\"model\": \"gpt-6.1-sol\"}, defaults 2,000 tokens in and 400 out). Price a workload at list prices across models: cost per request and per month for the requested model, its cheaper sibling, and the effect of prompt caching. Pricing table version 2026-10-28.",
      "inputSchema": {
        "properties": {
          "compare_to": {
            "items": {
              "type": "string"
            },
            "type": "array"
          },
          "input_tokens": {
            "description": "per request; default 2000",
            "type": "number"
          },
          "model": {
            "description": "model id, e.g. gpt-6.1-sol or claude-sonnet-5-5",
            "type": "string"
          },
          "output_tokens": {
            "description": "per request; default 400",
            "type": "number"
          },
          "requests_per_month": {
            "type": "number"
          },
          "static_tokens": {
            "description": "portion of input_tokens that is a fixed prefix (system + tools)",
            "type": "number"
          }
        },
        "required": [
          "model"
        ],
        "type": "object"
      },
      "name": "calculate_model_arbitrage"
    },
    {
      "description": "No API key needed. Rewrite an OpenAI Chat/Responses or Anthropic Messages request body so its static prefix is provider-cacheable (Anthropic cache_control breakpoints, OpenAI prompt_cache_key). Returns the optimized body, what changed, the token profile and the projected per-request saving. Never changes the tokens the model sees.",
      "inputSchema": {
        "properties": {
          "body": {
            "description": "The request body",
            "type": "object"
          },
          "endpoint": {
            "enum": [
              "/v1/chat/completions",
              "/v1/responses",
              "/v1/messages"
            ],
            "type": "string"
          },
          "strategy": {
            "enum": [
              "static",
              "conversation"
            ],
            "type": "string"
          }
        },
        "required": [
          "body"
        ],
        "type": "object"
      },
      "name": "optimize_prompt_payload"
    },
    {
      "description": "Create a TokenZero workspace and key with no form. Returns the key once, the base URLs that route through it (OPENAI_BASE_URL / ANTHROPIC_BASE_URL), the one-click dashboard link and the badge URL. Free up to the monthly free tier; nothing is billed.",
      "inputSchema": {
        "properties": {
          "name": {
            "description": "workspace name (default mcp-agent)",
            "type": "string"
          }
        },
        "type": "object"
      },
      "name": "provision_workspace"
    },
    {
      "description": "Live value scorecard for a TokenZero workspace: hard savings and net ROI per window, the four engines' contribution, speed, parity, availability and privacy posture, per-model breakdown and one-click recommendations. Needs the workspace key (tz_live_…); pass demo: true for a labelled sample.",
      "inputSchema": {
        "properties": {
          "demo": {
            "description": "return the sample scorecard instead",
            "type": "boolean"
          },
          "key": {
            "description": "workspace key (tz_live_…)",
            "type": "string"
          },
          "window": {
            "enum": [
              "24h",
              "7d",
              "30d",
              "all"
            ],
            "type": "string"
          }
        },
        "type": "object"
      },
      "name": "workspace_metrics"
    }
  ]
}

What the leaf hashes

{"body":"ddc65eb1222f557183d020e516072fcb70ca897c498c1ca0513b778041a5ab11","id":812650,"kind":"mcp.tools","method":"probe.mcp/1","observed_us":1793227960912349,"prev":766680,"subject":"e02856ce-442f-456c-a7b2-62fc1f55ed3c","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. 0x20e702f37108873261f2800667ca10efce8f5a9dc50236f670c887905f03f66c
2. 0x228ddd50eebb78cf51ca2a5b52be2f530fb56259f6446658ff4e6c66c0ae3aeb
3. 0x7aa19d3335a5a7cc2ac2f3bc1c667668c45853c284cf051bd24efc8f23c6bea4
4. 0xb27b513fdc375f451b0b4b6b0e40f11e96a0db39439ff8e71d8b60a6362dd2ec
5. 0xedf6fe8f328ca05efe12881a63358e343486ea34bd6f5ea8864b1c87e4567101
6. 0x61da7212e659924a016987084cd940f16da0dc9a69a8a9fa9c72aa9423b63b7f
7. 0x5b6961deb4e8b97f118fb76d64b8db48e1257dda2128b14e85b383ef5a76d8eb
8. 0xcf099841ba5ee1ed6721d8b607491c39698a708ca77d8051215d0d527794ac21
9. 0xf59d3884a43bfd4026895dea8b24f2d0a7a95bc540a1018aec517de340b8d636
10. 0x726f60245830f5664d861fce1dfed184d9ef1eb3618cd3abcde41850aef5a473
11. 0xfacf884a6c2b3e6139b10f5c7440d0885a3b19bf2389e3d4b6be90aec60612e8
12. 0xc93ece3e0a59b69f5b389bd42331a474e675f503f26ab5494a941fa4f5327261
13. 0x3ce83ad86ff4f1711072c47e954f682b6a67bc60b2342af27fa493f972b6b9c0
14. 0xa45c19ee1ddfa3971a75affe2da0c01e0a09406bb4b50c48e0d9cf0e082801a3
15. 0x4698ebc7cbcb2286f661b867355eb6a9183e884482ee16965889e886b17d19ac
16. 0x35e8995c30094446ec790423171382fccb377081b921295b0ae3840cc2c4f52c
17. 0xf078ea7d84d98dbeffed23d8e67b36751e60464ffd4cfdc005e0aa6f8cb9b459
18. 0xe027395fafb7fc41921c42ca0542524ea22204cbd5908b70b27f3d5fb26f5d0f

Day root

0x2c19037464cc4c493624565f01775d6446fc78eaa118fc6865bfe2bfca9ff78e

Chained

0xd1d7dee8322ceff8f4c8d83aa1fe4a4466825c2af1c97dbde3d35db61234f7be

Published

On Arc Testnet, block 69584042: 0x8bae2b1d097834c8b458513862d70d29ee807e98dc2e863b27250131c15b35ae