Evidence row 579705

What was observed

{
  "count": 8,
  "tools": [
    {
      "description": "Pro-tier. Fetch and analyze a web page, then audit it against the Proximens GEO Engine principles across all major GEO dimensions (structured data, crawler access, content depth, freshness, E-E-A-T, multimodal). INPUT: url (required, http/https); optional mode (\"fast\" = quick signal checks, returns in seconds — the default; \"deep\" = a full AI-synthesized consultancy report in Dutch with a 7-dimension scorecard and sector benchmark, takes ~30-50s), client_name (report header), branche_hint (\"main:sub\", e.g. \"health_wellness:yoga_studio\"), max_issues (1-25, default 10). RETURNS: JSON with a 0-100 score, severity-ranked issues (critical/major/minor) each with a finding and an actionable suggestion, top recommendations, and a markdown report; deep mode additionally returns score_set (7 GEO dimensions), sector (benchmark cohort), and a full consultancy-grade report_markdown (deep_mode=\"timeout_fallback\" means the synthesis exceeded its budget and the fast result was returned instead). USE fast mode for quick checks and bulk triage; USE deep mode when you need a client-ready audit report. Free tier is blocked.",
      "inputSchema": {
        "additionalProperties": false,
        "properties": {
          "branche_hint": {
            "description": "Branche hint in \"main:sub\" format, e.g. \"health_wellness:yoga_studio\". If omitted, principles are matched without branche filter.",
            "type": "string"
          },
          "client_name": {
            "description": "Optional client identifier for the audit report header",
            "type": "string"
          },
          "max_issues": {
            "default": 10,
            "description": "Maximum issues to return (default 10)",
            "maximum": 25,
            "minimum": 1,
            "type": "integer"
          },
          "mode": {
            "default": "fast",
            "description": "fast = quick signal checks (seconds); deep = full AI-synthesized consultancy report with sector benchmark (~30-50s)",
            "enum": [
              "fast",
              "deep"
            ],
            "type": "string"
          },
          "url": {
            "description": "Target URL to audit",
            "format": "uri",
            "type": "string"
          }
        },
        "required": [
          "url"
        ],
        "type": "object"
      },
      "name": "proximens_geo_audit_url"
    },
    {
      "description": "Pro-tier. Run many GEO-principle searches in a single fast call. INPUT: queries (array of 2-100 natural-language strings, each 3-500 chars); optional top_k_per_query (1-10, default 5) and category filter. RETURNS: JSON with a results array (per query: the query, its matched principles, and a count), plus total_queries, total_matches and processing time. USE WHEN you need many lookups at once, e.g. a full-site audit or a keyword list, instead of repeated search_principles calls.",
      "inputSchema": {
        "additionalProperties": false,
        "properties": {
          "category": {
            "enum": [
              "technical",
              "structured-data",
              "content",
              "ai-search",
              "freshness",
              "multimodal",
              "user-signals",
              "e-e-a-t",
              "mobile",
              "performance",
              "query-intent",
              "internal-linking",
              "other"
            ],
            "type": "string"
          },
          "queries": {
            "items": {
              "maxLength": 500,
              "minLength": 3,
              "type": "string"
            },
            "maxItems": 100,
            "minItems": 2,
            "type": "array"
          },
          "top_k_per_query": {
            "default": 5,
            "maximum": 10,
            "minimum": 1,
            "type": "number"
          }
        },
        "required": [
          "queries"
        ],
        "type": "object"
      },
      "name": "proximens_geo_bulk_search"
    },
    {
      "description": "Pro-tier. Fetch two web pages (your URL and a competitor's) and audit both against the Proximens GEO Engine principles using the same audit engine as audit_url, then compute the delta. INPUT: self_url and competitor_url (both required, http/https). RETURNS: JSON with a 0-100 score per URL (same scoring as audit_url), the principles each page satisfies, the principles each page VIOLATES that the other satisfies (delta_principles), and strategic insights on where to close the gap. USE WHEN you want a competitive GEO gap analysis between your page and a rival's.",
      "inputSchema": {
        "additionalProperties": false,
        "properties": {
          "competitor_url": {
            "description": "Competitor URL to compare against",
            "format": "uri",
            "type": "string"
          },
          "self_url": {
            "description": "Your URL to audit",
            "format": "uri",
            "type": "string"
          }
        },
        "required": [
          "self_url",
          "competitor_url"
        ],
        "type": "object"
      },
      "name": "proximens_geo_compare_urls"
    },
    {
      "description": "Fetch one GEO principle from the Proximens GEO Engine by its UUID. INPUT: id (UUID, normally taken from a prior search_principles result). RETURNS: a single principle as JSON with id, title, summary, category and confidence; Pro/Enterprise tiers additionally return full_text, source_url, source_type, evidence_count and the last-validated timestamp. USE WHEN you already have a principle id and need its full detail — typically to drill down after search_principles.",
      "inputSchema": {
        "additionalProperties": false,
        "properties": {
          "id": {
            "description": "Principle UUID (from search_principles results)",
            "format": "uuid",
            "type": "string"
          }
        },
        "required": [
          "id"
        ],
        "type": "object"
      },
      "name": "proximens_geo_get_principle"
    },
    {
      "description": "Return live aggregate statistics for the Proximens GEO Engine knowledge base. INPUT: none. RETURNS: JSON with total_principles (high-confidence count), total_categories, and on Pro/Enterprise also extended quality metrics (full corpus size and a confidence_distribution) plus the last-validated timestamp. USE WHEN you need to gauge the size and quality of the corpus before relying on it.",
      "inputSchema": {
        "additionalProperties": false,
        "description": "No input parameters",
        "properties": {},
        "type": "object"
      },
      "name": "proximens_geo_get_stats"
    },
    {
      "description": "List the GEO principle taxonomy of the Proximens GEO Engine with a live count of high-confidence principles per category. INPUT: none. RETURNS: JSON with a categories array of {category, count, description} sorted by count, plus a reconciled total that matches get_stats.total_principles. Categories: technical, structured-data, ai-search, content, e-e-a-t, freshness, multimodal, user-signals, performance, query-intent, internal-linking, mobile, other. USE WHEN you want to discover which categories exist before narrowing a search_principles call with the category filter.",
      "inputSchema": {
        "additionalProperties": false,
        "description": "No input parameters",
        "properties": {},
        "type": "object"
      },
      "name": "proximens_geo_list_categories"
    },
    {
      "description": "Semantic search over the Proximens GEO Engine: a curated, continuously-updated knowledge base of 4.000+ verified Generative Engine Optimization (GEO/AEO) principles, each graded by a 0-1 confidence score and traceable to a verified source. INPUT: query (natural language, 3-500 chars); optional category (one of 13 GEO categories), top_k (1-25, default 10), min_confidence (0-1, default 0.5). RETURNS: ranked principles as JSON, each with id, title, summary, category, confidence and a relevance score; Pro/Enterprise tiers additionally return full_text and source. USE WHEN you need evidence-backed answers about how AI search engines (ChatGPT, Perplexity, Gemini, Google AI Overviews, Copilot) select, rank and cite web content.",
      "inputSchema": {
        "additionalProperties": false,
        "properties": {
          "category": {
            "description": "Filter by category (one of 13 GEO categories)",
            "enum": [
              "technical",
              "structured-data",
              "content",
              "ai-search",
              "freshness",
              "multimodal",
              "user-signals",
              "e-e-a-t",
              "mobile",
              "performance",
              "query-intent",
              "internal-linking",
              "other"
            ],
            "type": "string"
          },
          "min_confidence": {
            "default": 0.5,
            "description": "Minimum confidence score (0-1). Default 0.5 filters noise; raise to 0.8+ for high-confidence claims only",
            "maximum": 1,
            "minimum": 0,
            "type": "number"
          },
          "query": {
            "description": "Natural-language search query (e.g. \"schema markup for local businesses\" or \"how to optimize for ChatGPT citations\")",
            "maxLength": 500,
            "minLength": 3,
            "type": "string"
          },
          "top_k": {
            "default": 10,
            "description": "Number of principles to return (max 25)",
            "maximum": 25,
            "minimum": 1,
            "type": "integer"
          }
        },
        "required": [
          "query"
        ],
        "type": "object"
      },
      "name": "proximens_geo_search_principles"
    },
    {
      "description": "Generate a structured, GEO-optimized content brief for a topic using the Proximens GEO Engine. INPUT: topic (3-200 chars); optional target_branche (one of 7 verticals), word_count_target (300-5000, default 1500) and up to 3 competitor_urls. RETURNS: JSON with a suggested H1 and H2 section structure with key points, the principles the content should address, and (Pro/Enterprise) FAQ suggestions and recommended schema.org markup. USE WHEN you need to brief a writer so a page is built to be cited by AI search engines.",
      "inputSchema": {
        "additionalProperties": false,
        "properties": {
          "competitor_urls": {
            "items": {
              "format": "uri",
              "type": "string"
            },
            "maxItems": 3,
            "type": "array"
          },
          "target_branche": {
            "enum": [
              "local_services",
              "digital_services",
              "product_commerce",
              "creative_professional",
              "health_wellness",
              "b2b_saas",
              "universal"
            ],
            "type": "string"
          },
          "topic": {
            "maxLength": 200,
            "minLength": 3,
            "type": "string"
          },
          "word_count_target": {
            "default": 1500,
            "maximum": 5000,
            "minimum": 300,
            "type": "number"
          }
        },
        "required": [
          "topic"
        ],
        "type": "object"
      },
      "name": "proximens_geo_synthesize_brief"
    }
  ]
}

What the leaf hashes

{"body":"e120796402d0e408c8cb8a5d7724fe9cd6abe324512e7265837c06f4b457bd59","id":579705,"kind":"mcp.tools","method":"probe.mcp/1","observed_us":1793143536130430,"prev":null,"subject":"eca38e02-56cf-434f-8c4b-59112cf86c8b","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. 0xd8aead27f7c418e4e893754ebfef244b9db10fad2571b703e85e1ee8766c3c35
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6. 0xa9164fbf19974ba09147410adc4e0647dbb7ec853ffdeee8673717f576ccbce8
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8. 0xdbda9d06f14c6999bd8c996ef3198e79e1e42a2d8641fc0f0d3844a3738ea9b3
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10. 0xb53d1c4a76e033644f89ac53f7cf54305fe5dc930aec990b2077c8a49cdfeb26
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19. 0x9fc354c0ea3efc3f1b6548bb77ea902a50f2c9647647ea61592efce2bd795699

Day root

0xdd77e5e99da32c3f154180d63e3c3e6fe11e896a2c6896412ef6b5c4bcf7e795

Chained

0x23bb4dfd09f55c15ac3962f53578e02c6114d5bfd9bb451cf8794fb6e2817ef4

Published

On Arc Testnet, block 69414147: 0xb2ebd83aafeb765a0ce66e76aefb133ed06606c98038fd56191e2bcd1dcb77b4