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Evidence row 766683

Observed , by probe.mcp/1.

Kind
mcp.tools
About
endpoint 4ae7b41c-60da-4ff7-994c-e845fb4edd7f
Recorded
Previous row
#540992
Body SHA-256
3449bd1410879729508493ce829e08e25609fc33938f7c96ffc58e9e55755f9f
Leaf
0x88a47c46b7d6cb5df7b163974cddc071d68beb7724d212bc7e831e1ab9fad4fa

What was observed

{
  "count": 5,
  "tools": [
    {
      "description": "Static-analyze a public GitHub 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. 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": "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 2026-10-27.",
      "inputSchema": {
        "properties": {
          "compare_to": {
            "items": {
              "type": "string"
            },
            "type": "array"
          },
          "input_tokens": {
            "type": "number"
          },
          "model": {
            "type": "string"
          },
          "output_tokens": {
            "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",
          "input_tokens",
          "output_tokens"
        ],
        "type": "object"
      },
      "name": "calculate_model_arbitrage"
    },
    {
      "description": "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":"3449bd1410879729508493ce829e08e25609fc33938f7c96ffc58e9e55755f9f","id":766683,"kind":"mcp.tools","method":"probe.mcp/1","observed_us":1793180494298897,"prev":540992,"subject":"4ae7b41c-60da-4ff7-994c-e845fb4edd7f","subject_kind":"endpoint"}

Proof

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

  1. 0x88a4e68f9e8adc15f68ab130a96d10ec4724f57765852e4efbc7ea1b0c608b3f
  2. 0xe93fa35411ae0e0ca691bf642e67913d55ce2b9057bd1975164192c3fa83e8a4
  3. 0x0ac1245bc415fcfac9732f70aa364644cfc9922753c035703a76add19b59fd2a
  4. 0x09ce47e1149d771775d21a1ff3c2b15f9710e58420d2bc8345ff828d882e3828
  5. 0xed93a960ce44b6e47ffeb2b0b48142bfb9235e0f3a353c886d90b96bad3d26b8
  6. 0x38323e7d96695b9aca99070aca19cbe7875c87cd0e79a4aa286ca2c1474fe975
  7. 0x9d811ed5cdecc5ecc764964034fd9999e8a08bc9b5581a705b41582a251273a7
  8. 0x2034b978b7c70b2efdad592a29bc21c24e4119509d634c63028623cd3e9229e1
  9. 0x9f350fbe016f58d978a52f9619cc23c7eefd0e256455c844b0ee2a3db2a88016
  10. 0x47775bd6d522958071dab25849da06f0b89d3ced43b2aebbb8383f605ef8c645
  11. 0xfcb87fd1f97629d9bbe729e1af67ec8754514bfe9ee1cc45cf69c5e026b58ce0
  12. 0x1810cd44ebfe5f26ef4925cdfbe26287c4f9dbf1cd3343ea6968181a15656337
  13. 0x73b20f1c980fc42d6a2767acddce2cb41764e0b54fa3342f0dc75bd609f6023e
  14. 0x115e196b75a0422533397227b51b9c807591ec2574919c03800bb0d253f110e8
  15. 0xe2f9c120d14f36fdddf9b4e605c5d0b8a13b835e2380472255dc22e14b93e84e
  16. 0x74cf00e07b52415c2184cf20be69d195ed2725497a6892c2f3e449497c17d0ec
  17. 0x7a743a0a09057bae687051274e1d45bf973bd36a1a4ba16a191b5d29e3bdfa2a
  18. 0xe027395fafb7fc41921c42ca0542524ea22204cbd5908b70b27f3d5fb26f5d0f
Day root
0x2c19037464cc4c493624565f01775d6446fc78eaa118fc6865bfe2bfca9ff78e
Chained
0xd1d7dee8322ceff8f4c8d83aa1fe4a4466825c2af1c97dbde3d35db61234f7be
Published
On Arc Testnet, block 69584042: 0x8bae2b1d097834c8b458513862d70d29ee807e98dc2e863b27250131c15b35ae

How to check it · This row as JSON