Evidence row 579437

What was observed

{
  "count": 12,
  "tools": [
    {
      "description": "Ask a natural-language question and receive structured intelligence context retrieved from Tresslers Group dossiers via RAG (Retrieval Augmented Generation). Returns relevant document chunks, source citations, conviction metadata, and graph neighborhood data. The calling LLM should synthesize the returned context into a coherent answer.",
      "inputSchema": {
        "properties": {
          "max_sources": {
            "description": "Maximum number of source dossier chunks to retrieve (default 5, max 8).",
            "type": "number"
          },
          "question": {
            "description": "Natural language question (e.g., 'What are the key geopolitical risks in sovereign AI policy?').",
            "type": "string"
          }
        },
        "required": [
          "question"
        ],
        "type": "object"
      },
      "name": "ask_intelligence_rag"
    },
    {
      "description": "Batch codon optimization for teams and agents: optimize up to 50 protein or cDNA coding sequences in one call with the Logos bio-engine (CAI maximization, GC harmonization, restriction-site avoidance, consensus leaders). Free tier: 100 sequences per caller per day. Overage is $0.50 USDC per 10 sequences ($0.05/sequence) on Base, verified on-chain — pass payment_proof when the batch exceeds the free tier. Research Use Only (RUO); select-agent sequences prohibited.",
      "inputSchema": {
        "properties": {
          "api_key": {
            "description": "Optional stable caller identifier for quota tracking (useful behind shared IPs). Self-asserted in v1.",
            "type": "string"
          },
          "gc_target": {
            "description": "Target global GC percentage (e.g., 52 for E. coli, 58 for Human). Optional.",
            "type": "number"
          },
          "host": {
            "description": "Target expression organism: 'e_coli', 'h_sapiens' (HEK293), 'c_griseus' (CHO), 's_cerevisiae', 'p_pastoris', 'v_natriegens', 'n_benthamiana', 'a_thaliana'. Default: 'e_coli'",
            "enum": [
              "e_coli",
              "h_sapiens",
              "c_griseus",
              "s_cerevisiae",
              "p_pastoris",
              "v_natriegens",
              "n_benthamiana",
              "a_thaliana"
            ],
            "type": "string"
          },
          "payment_proof": {
            "description": "USDC transaction hash on Base. Required when the batch exceeds the 100/day free tier.",
            "type": "string"
          },
          "sequences": {
            "description": "Amino acid sequences (single-letter uppercase) or DNA coding sequences to optimize. Max 50 per call; max 5000 characters each.",
            "items": {
              "type": "string"
            },
            "maxItems": 50,
            "type": "array"
          }
        },
        "required": [
          "sequences"
        ],
        "type": "object"
      },
      "name": "batch_optimize_codon"
    },
    {
      "description": "Retrieves prediction market Alpha Gaps comparing Tresslers Group Bayesian model odds against Polymarket order books, along with Brier scores, calibration alpha (+3,480 bps benchmark), and cryptographic SHA-256 pre-commitments.",
      "inputSchema": {
        "properties": {
          "category": {
            "description": "Optional filter by strategic intelligence domain.",
            "type": "string"
          },
          "limit": {
            "description": "Maximum number of Alpha Gap records to return.",
            "type": "number"
          },
          "min_divergence": {
            "description": "Minimum absolute divergence gap in percentage points (e.g. 20 for >= 20% gap).",
            "type": "number"
          },
          "settlement_type": {
            "description": "Filter by settlement tier: 'contract_settled', 'outcome_tracked', 'unscored_archive', or 'all'.",
            "enum": [
              "all",
              "contract_settled",
              "outcome_tracked",
              "unscored_archive"
            ],
            "type": "string"
          },
          "status": {
            "description": "Filter by status or settlement type (e.g., 'CONTRACT_SETTLED', 'OUTCOME_TRACKED', 'RESOLVED', 'PENDING', 'ALL').",
            "enum": [
              "ALL",
              "RESOLVED",
              "PENDING",
              "CONTRACT_SETTLED",
              "OUTCOME_TRACKED"
            ],
            "type": "string"
          }
        },
        "type": "object"
      },
      "name": "get_alpha_gap"
    },
    {
      "description": "Queries intelligence updates and conviction deltas attested since a specific UTC timestamp.",
      "inputSchema": {
        "properties": {
          "since_timestamp": {
            "description": "ISO UTC timestamp (e.g., '2026-05-01T00:00:00Z').",
            "type": "string"
          }
        },
        "required": [
          "since_timestamp"
        ],
        "type": "object"
      },
      "name": "get_dossier_delta"
    },
    {
      "description": "Queries the public calibration and backtest ledger of Tresslers Group sovereign intelligence models. Supports filtering by settlement type ('contract_settled' for live prediction markets, 'outcome_tracked' for internal foresight models, 'unscored_archive' for pre-commitment history, or 'all'). Returns Brier calibration scores, transparent failure analysis, and verifiable primary source URLs.",
      "inputSchema": {
        "properties": {
          "category": {
            "description": "Optional filter by intelligence pillar/category.",
            "type": "string"
          },
          "settlement_type": {
            "description": "Filter by settlement tier: 'contract_settled' (live prediction market order-books), 'outcome_tracked' (internal Bayesian foresight), 'unscored_archive' (pre-commitment era), or 'all' (default).",
            "enum": [
              "all",
              "contract_settled",
              "outcome_tracked",
              "unscored_archive"
            ],
            "type": "string"
          },
          "status": {
            "description": "Filter by resolution status (default: 'ALL').",
            "enum": [
              "ALL",
              "RESOLVED",
              "PENDING"
            ],
            "type": "string"
          }
        },
        "required": [],
        "type": "object"
      },
      "name": "get_model_record"
    },
    {
      "description": "Returns a list of all available intelligence dossier slugs.",
      "inputSchema": {
        "properties": {},
        "required": [],
        "type": "object"
      },
      "name": "list_dossiers"
    },
    {
      "description": "Optimizes a protein (amino acid) or cDNA coding sequence for maximal recombinant expression yield in a target host organism using the Logos biocomputing engine. Executes Codon Adaptation Index (CAI) maximization, GC-content harmonization, restriction site avoidance, and ribosomal consensus leader generation. Research Use Only (RUO). In-silico modeling only; select-agent and regulated pathogen sequence optimization is strictly prohibited under 42 CFR 73 and BWC compliance. Free and open to all AI agents.",
      "inputSchema": {
        "properties": {
          "gc_target": {
            "description": "Target global GC percentage (e.g., 52 for E. coli, 58 for Human). Optional.",
            "type": "number"
          },
          "host": {
            "description": "Target expression organism: 'e_coli', 'h_sapiens' (HEK293), 'c_griseus' (CHO), 's_cerevisiae', 'p_pastoris', 'v_natriegens', 'n_benthamiana', 'a_thaliana'. Default: 'e_coli'",
            "enum": [
              "e_coli",
              "h_sapiens",
              "c_griseus",
              "s_cerevisiae",
              "p_pastoris",
              "v_natriegens",
              "n_benthamiana",
              "a_thaliana"
            ],
            "type": "string"
          },
          "sequence": {
            "description": "Amino acid sequence (single-letter uppercase e.g. 'MSKGEELFT...') or DNA coding sequence to optimize.",
            "type": "string"
          }
        },
        "required": [
          "sequence"
        ],
        "type": "object"
      },
      "name": "optimize_codon"
    },
    {
      "description": "Traverses the multi-hop conceptual knowledge graph across entities (GraphRAG). Free preview available for direct 1-hop adjacencies. Deeper reasoning (2-3 hops) requires x402 settlement.",
      "inputSchema": {
        "properties": {
          "depth": {
            "description": "Graph traversal hop depth (1 for free preview, 2 to 3 for deep reasoning).",
            "type": "number"
          },
          "entity_id": {
            "description": "Target conceptual entity or dossier slug (e.g., 'Quantum AI', 'ThinkForge').",
            "type": "string"
          },
          "payment_proof": {
            "description": "Standard x402 signed USDC transaction proof (0x...). Required for multi-hop graph access.",
            "type": "string"
          }
        },
        "required": [
          "entity_id"
        ],
        "type": "object"
      },
      "name": "query_knowledge_graph"
    },
    {
      "description": "Reads the pure text contents of a specific intelligence dossier. Requires x402 payment proof for premium intelligence. Without payment proof, returns a high-fidelity strategic excerpt and payment challenge.",
      "inputSchema": {
        "properties": {
          "payment_proof": {
            "description": "Standard x402 signed USDC transaction proof (0x...). Required for full decrypted access.",
            "type": "string"
          },
          "slug": {
            "description": "The slug of the dossier to read (e.g., 'sovereign-ai-state-national-policy-2026').",
            "type": "string"
          }
        },
        "required": [
          "slug"
        ],
        "type": "object"
      },
      "name": "read_dossier"
    },
    {
      "description": "Searches and filters across all published Tresslers Group intelligence dossiers by keywords, strategic domain/category, tags, or conviction threshold.",
      "inputSchema": {
        "properties": {
          "category": {
            "description": "Optional category filter (e.g., 'Geopolitics & Sovereign Policy', 'Energy & Infrastructure').",
            "type": "string"
          },
          "limit": {
            "description": "Maximum number of dossiers to return (default: 5, max: 20).",
            "type": "number"
          },
          "min_conviction": {
            "description": "Minimum conviction threshold (0.0 to 1.0, e.g., 0.85).",
            "type": "number"
          },
          "query": {
            "description": "Search query or keywords to match across title, excerpt, content, and tags.",
            "type": "string"
          },
          "tag": {
            "description": "Optional tag filter (e.g., 'sovereign-ai', 'energy-transition').",
            "type": "string"
          }
        },
        "required": [
          "query"
        ],
        "type": "object"
      },
      "name": "search_dossiers"
    },
    {
      "description": "Executes a semantic vector similarity search across the entire ThinkForge intelligence substrate. Returns relevant snippets and strategic conviction metadata.",
      "inputSchema": {
        "properties": {
          "filter": {
            "description": "Optional SQL-like metadata filter (e.g., 'convictionScore >= 0.85').",
            "type": "string"
          },
          "limit": {
            "description": "Max results to return (default 5, max 10).",
            "type": "number"
          },
          "query": {
            "description": "Semantic research question (e.g., 'What are the geopolitical vulnerabilities in green hydrogen supply chains?').",
            "type": "string"
          }
        },
        "required": [
          "query"
        ],
        "type": "object"
      },
      "name": "search_intelligence_matrix"
    },
    {
      "description": "Cryptographically verifies a predictive alpha pre-commitment against the immutable SHA-256 ledger using either a 64-character hash digest or canonical preimage parameters.",
      "inputSchema": {
        "properties": {
          "canonical_preimage": {
            "description": "Alternative key for canonical preimage string.",
            "type": "string"
          },
          "dispatch_date": {
            "description": "Dispatch date component (YYYY-MM-DD).",
            "type": "string"
          },
          "dossier_slug": {
            "description": "Target dossier slug component.",
            "type": "string"
          },
          "hash": {
            "description": "64-character lowercase hexadecimal SHA-256 commitment digest.",
            "type": "string"
          },
          "market_question": {
            "description": "Exact market question component.",
            "type": "string"
          },
          "model_odds": {
            "description": "Model odds integer component (0-100).",
            "type": "number"
          },
          "preimage": {
            "description": "Full pipe-delimited UTF-8 canonical preimage string (YYYY-MM-DD|question|model_odds|target_dossier_slug).",
            "type": "string"
          }
        },
        "type": "object"
      },
      "name": "verify_commitment"
    }
  ]
}

What the leaf hashes

{"body":"a2beb54ab674ca12a00ce5f5621d242722a692cf2503b239d9b4ff05338915da","id":579437,"kind":"mcp.tools","method":"probe.mcp/1","observed_us":1793143424539560,"prev":null,"subject":"6d1c052e-fc2e-4472-9326-effb7bd8497f","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. 0x93719a4951d92e17843ac66c380af080ec067901734a1ebe514d35277fff8196
2. 0xd8a9f0c4e651e22a4c1a9359e54b5ff8f409ba8bbc5a6cabcda018524e0c1706
3. 0xe9638ad0c49758cc15e1759fd88fb269df2dac281f48cd3641b50a8feae2c5ef
4. 0x0fa4bfdfbb6141ea62fc97ea0933217d12dfeec7809a85d1af574313d45592e3
5. 0x52f1d3cfb6200cdd73ce094b1a5c7e2a086dbf73e54f3d599ec91d7f853647ba
6. 0x140545d3b1836dd8770db62c5d58eeabcd2aa3c29933506d5613e50073f0485b
7. 0xee09d53670be8ac136b7b6b33021f5df0c67da90e5d34721254d6c6bc70b9b50
8. 0x76f5ee8671d6df002298d3f24c833473d9a6c051f77cdbd1d4cd104c97182417
9. 0x5f7cab3b760fce47b9437889ee0d1ab04f2245d92b890c40ec06253603022f00
10. 0x001e2fbd0c3e79f4abf52eea92556b3364585e138443456e12c92a9bbc0e53a3
11. 0x223b6ff70aa897a0d2181f63f8038457f6caafaff29251cc49e8dd36940a8514
12. 0x21b50531db6db8c65523fd5559cda8006afb66474d66107f3c29e6cd41ec3fbe
13. 0x59c5c792b40d9054ce2b84a60af97fa54f2e1a2a369a6a52dc3f45de41128e97
14. 0xa3c9c760ee424c178bab782711c2b9d3e15a80a6fde99165972cb75b306bb8b4
15. 0xb4f7223140c8a36cb87a63d77cf617086d9968dc4658f03e102571ebe06e9ed6
16. 0xfecf3d4646dd4984a13dd715499e842c3fde63692336d97394c2bfd8e877ca5d
17. 0xf30fddbf4811226c58e8f0daf36fddd79fc5f1356b592bb1d99a8061ddbabe66
18. 0x2bf8151e5f157dd183b9de6fa00469551932a7a6e3f33a694895000ac9061197
19. 0x9fc354c0ea3efc3f1b6548bb77ea902a50f2c9647647ea61592efce2bd795699

Day root

0xdd77e5e99da32c3f154180d63e3c3e6fe11e896a2c6896412ef6b5c4bcf7e795

Chained

0x23bb4dfd09f55c15ac3962f53578e02c6114d5bfd9bb451cf8794fb6e2817ef4

Published

On Arc Testnet, block 69414147: 0xb2ebd83aafeb765a0ce66e76aefb133ed06606c98038fd56191e2bcd1dcb77b4