Evidence row 309708

What was observed

{
  "count": 12,
  "tools": [
    {
      "description": "Compares a description of a project, business or problem against a fixed published rule and returns a verdict: good_fit, mixed, poor_fit or insufficient_information. Wording alone never produces poor_fit. Where the text touches one of the things Ashley turns down, the result lists questions in ask_these_then_call_again, and the three named fields take the person's own answers on a later call. poor_fit needs an explicit yes in one of those fields; good_fit needs an explicit no in all three. Having a development team is not the same as wanting Ashley to supply one.",
      "inputSchema": {
        "additionalProperties": false,
        "properties": {
          "must_start_within_a_week": {
            "description": "Does the work have to start within a week? Starting within two weeks is the normal target, so that alone is 'no'.",
            "enum": [
              "yes",
              "no",
              "unsure"
            ],
            "type": "string"
          },
          "situation": {
            "description": "What the person is trying to build or solve, their stage, team and constraints. Free text.",
            "type": "string"
          },
          "wants_a_delivery_team": {
            "description": "Does the person want Ashley to SUPPLY a team? Leading a team they already have is 'no'.",
            "enum": [
              "yes",
              "no",
              "unsure"
            ],
            "type": "string"
          },
          "wants_spec_built_without_challenge": {
            "description": "Do they want a finished spec built exactly as written, with no room to challenge it? A complete brief to quote from is 'no'.",
            "enum": [
              "yes",
              "no",
              "unsure"
            ],
            "type": "string"
          }
        },
        "required": [
          "situation"
        ],
        "type": "object"
      },
      "name": "assess_fit"
    },
    {
      "description": "Which AI techniques he has built in production (prompting, retrieval, fine-tuning, MCP servers, local inference) and when each one is the right choice. Use this for questions about AI, automation or data privacy.",
      "inputSchema": {
        "additionalProperties": false,
        "properties": {},
        "type": "object"
      },
      "name": "get_ai_capability"
    },
    {
      "description": "Full detail for one engagement: the situation, what was built, the engineering decisions and why they were made, the stack, and where there is one, the commercial outcome in indexed figures.",
      "inputSchema": {
        "additionalProperties": false,
        "properties": {
          "slug": {
            "description": "Case study slug from list_work, e.g. 'coding-education'.",
            "type": "string"
          }
        },
        "required": [
          "slug"
        ],
        "type": "object"
      },
      "name": "get_case_study"
    },
    {
      "description": "How to reach Ashley: email, the contact page, and a booking link when one is live. Use when the person wants to make contact.",
      "inputSchema": {
        "additionalProperties": false,
        "properties": {},
        "type": "object"
      },
      "name": "get_contact"
    },
    {
      "description": "Where the numbers on the site come from, for when someone asks whether they can be trusted. Returns one statement of provenance: the figures are counts from live systems, self-reported with a stated method and not independently audited, and Ashley will run any of them in front of someone who asks. Per-figure detail (the collections, queries and returned values) is not published.",
      "inputSchema": {
        "additionalProperties": false,
        "properties": {},
        "type": "object"
      },
      "name": "get_evidence"
    },
    {
      "description": "The questions buyers ask, with his answers. Pass a query to get the relevant ones; omit it to list every question without the answers.",
      "inputSchema": {
        "additionalProperties": false,
        "properties": {
          "query": {
            "description": "Keywords, e.g. 'price', 'team', 'ill', 'equity'.",
            "type": "string"
          }
        },
        "type": "object"
      },
      "name": "get_faqs"
    },
    {
      "description": "The complete text of one article, by slug. Use after search_insights, and only when the summary is not enough to answer the question.",
      "inputSchema": {
        "additionalProperties": false,
        "properties": {
          "slug": {
            "description": "The article slug, from search_insights.",
            "type": "string"
          }
        },
        "required": [
          "slug"
        ],
        "type": "object"
      },
      "name": "get_insight"
    },
    {
      "description": "Positioning, background and what makes him different from other fractional CTOs. Start here.",
      "inputSchema": {
        "additionalProperties": false,
        "properties": {},
        "type": "object"
      },
      "name": "get_overview"
    },
    {
      "description": "What this connector and the website do with data, and what they do not. Use this if the person asks about privacy, tracking, cookies, GDPR, or what Ashley can see of this conversation.",
      "inputSchema": {
        "additionalProperties": false,
        "properties": {},
        "type": "object"
      },
      "name": "get_privacy"
    },
    {
      "description": "The three ways an engagement runs (Build, Partner, Consult), the fixed-price diagnostic most engagements start with, how the process works, and which client surfaces he builds: desktop applications, browser extensions, chat-platform integrations and web. Use this for any question about platforms, apps, pricing or how an engagement is structured.",
      "inputSchema": {
        "additionalProperties": false,
        "properties": {},
        "type": "object"
      },
      "name": "get_services"
    },
    {
      "description": "Summaries of the selected work, with the slug needed for get_case_study.",
      "inputSchema": {
        "additionalProperties": false,
        "properties": {},
        "type": "object"
      },
      "name": "list_work"
    },
    {
      "description": "Search his articles on technical leadership, fractional CTO work, AI and building SaaS. Returns titles, summaries and the matching passage. Call get_insight with a slug for the full text of one.",
      "inputSchema": {
        "additionalProperties": false,
        "properties": {
          "query": {
            "description": "Keywords to match against title, summary and body.",
            "type": "string"
          }
        },
        "type": "object"
      },
      "name": "search_insights"
    }
  ]
}

What the leaf hashes

{"body":"562b82da5e93214d55d0c3af5dd16638a0f448dc13902096524605f011012d59","id":309708,"kind":"mcp.tools","method":"probe.mcp/1","observed_us":1793059070263903,"prev":null,"subject":"d471e27d-8d5e-4b72-8e29-cbfff4af9927","subject_kind":"endpoint"}

Proof

Fold these into the leaf in order, hashing each sorted pair with keccak256, to reach the root of 2026-10-26 (296695 rows).

1. 0x2b45e1747c9324b88f269b31d211250d82baf02fcdd5297776e7a9e3f90a3544
2. 0xd63e4e59f1ba7b38b9471ffa511a2da13bc71e272c9f344a9d5b8a28855e723f
3. 0xef07ce7a98c40f9b989d1c19f6cb1f543a88dd85090cb28d227f08748c640c83
4. 0x96520e6e41bfa6a12fa9c655850e48a5c20b12a59f4bc907dd4c1f7c0b54a891
5. 0xd12fde7743e982d12d4e6dccc4a8b2d2b164ec9a23f47b7a7b19a76a46d661d1
6. 0x07d22e2b17c081dd8f0e3e1492014726883b40fe78b3732c1524adad5b43069f
7. 0x04eb834e74309bc8b74e3032cde0c7dea06ece2726ec02a6bf7d100cbe32be0e
8. 0x1589fb832aba315e75246698da85fb022f60acf39ad2b8264eb429dda48eb863
9. 0xf39a9d1a20c443ec85e4c92007dcbfa1e0ec69cd730017ccdadac95f3c0f417d
10. 0x755e8bb86bef8a205359ea18e6bb499124975e318b315b74b91ac3dddeb77d5e
11. 0xde76827525517f5c8aef4ffd7831eab09ee63757dcc2282bc64307fd6ee67ad1
12. 0xe8bf822fd6c66b233e3e13e08596e8fb41c1ed9b85be0e4d50cfc9b740c0e206
13. 0xb87e8b32cda179c9e632e227bbd8a6d1f6f61d9d81bb92dbd4dd835d2b3b5caa
14. 0x683955a5b2250ad50568d022a6580d8739b6aff1356779d72a773b088e7c289c
15. 0x610738e0c43687aded83c613c73de8b5226c73c375858a167436461c522ea213
16. 0x06d71bfa7d8b41d62c8836efc2c601e1cc3772d9453596b2ae4950887f3310ca
17. 0x8c756542e482157df0c36464d75c937b845e4423dbbcfad08f0cf8d5a16b9a13
18. 0x20704208aa6923d6b2450ef74ede87a6e0265d46447926038fe9506e9e154b2d
19. 0x9b9ce5b74b9bab5121612ef6b64920a8d453cbf311096a423eef9702747e868a

Day root

0xf43e8dcc3d451a4788b26be904c55e8c4c0fa75c9666796c51db38d37e2c0df8

Chained

0x702d5ac508f08b0d1accf36af25822d8e5660367494edd17a642fb61e158a57a

Published

On Arc Testnet, block 69251483: 0x495162f8936e19f0039918feebe6c307f327cd6bf8c37219620b1970041fa9ae