Evidence row 291577

What was observed

{
  "count": 14,
  "tools": [
    {
      "description": "Evaluates a worker agent's submitted task deliverable against agreed contract acceptance criteria to issue a deterministic escrow settlement verdict ('RELEASE_FUNDS_TO_WORKER_AGENT', 'REQUEST_REVISION', or 'REFUND_CLIENT'). Use when settling multi-agent bounties or verifying subcontractor deliverables before releasing MPP/L402 escrow funds; for checking syntactic JSON Schema property types without evaluating semantic contract terms, use enforce_json_schema_types instead. Read-only call (32 KB, 60 RPM): accepts 'deliverable' (with enum defaults), uses '?demo=true' or MPP (30 sats); HTTP 400 if invalid.",
      "inputSchema": {
        "properties": {
          "arbitration_standard": {
            "description": "Strictness of contract verification; defaults to 'strict_all_criteria' when omitted.",
            "enum": [
              "strict_all_criteria",
              "substantial_completion",
              "schema_only"
            ],
            "type": "string"
          },
          "deliverable": {
            "description": "JSON string or text deliverable submitted by the worker agent.",
            "type": "string"
          },
          "terms": {
            "description": "Contract acceptance criteria that the deliverable must satisfy; defaults to 'Complete verified JSON deliverable' when omitted.",
            "type": "string"
          }
        },
        "required": [
          "deliverable"
        ],
        "type": "object"
      },
      "name": "arbitrate_agent_deliverable_escrow"
    },
    {
      "description": "Deduplicates repetitive lines or records, normalizes Unicode whitespace, strips HTML/control noise, and canonicalizes tabular text or JSON arrays. Use when cleaning noisy scraped lists, CSV rows, or duplicate log lines before indexing or analysis; for extracting typed JSON fields from free-form prose paragraphs, use extract_structured_json_entities instead. Stateless read-only edge call (max 32 KB, 60 RPM): accepts 'data' ('normalization_mode' defaults to 'exact_match'), supports '?demo=true' or L402 (15 sats), and returns HTTP 400 if invalid.",
      "inputSchema": {
        "properties": {
          "data": {
            "description": "Raw newline-delimited text, CSV rows, or JSON array string to deduplicate and normalize.",
            "type": "string"
          },
          "normalization_mode": {
            "description": "Deduplication key sensitivity; defaults to 'case_insensitive_trim' when omitted.",
            "enum": [
              "exact_match",
              "case_insensitive_trim",
              "alphanumeric_canonical"
            ],
            "type": "string"
          }
        },
        "required": [
          "data"
        ],
        "type": "object"
      },
      "name": "clean_tabular_dataset"
    },
    {
      "description": "Compiles a raw task description into a structured XML system prompt (<role>, <context>, <objective>, <constraints>, <workflow>, <output_format>) engineered to prevent instruction drift. Use when authoring or upgrading a system prompt for an autonomous agent or sub-agent workflow; for mapping a task objective to a concrete sequence of MCP tool calls, use route_mcp_workflow_plan instead. Stateless read-only edge call (max 32 KB, 60 RPM): accepts 'task' ('prompt_style' defaults to 'anthropic_xml_tags'), supports '?demo=true' or L402 (20 sats), and returns HTTP 400 if invalid.",
      "inputSchema": {
        "properties": {
          "prompt_style": {
            "description": "Structural formatting template; defaults to 'anthropic_xml_tags' when omitted.",
            "enum": [
              "anthropic_xml_tags",
              "markdown_sections",
              "strict_json_responder"
            ],
            "type": "string"
          },
          "task": {
            "description": "Raw task instructions, persona goal, and operational constraints to compile.",
            "type": "string"
          }
        },
        "required": [
          "task"
        ],
        "type": "object"
      },
      "name": "compile_xml_agent_prompt"
    },
    {
      "description": "Validates a raw LLM output object against a target JSON Schema, automatically coercing mismatched scalar types (e.g. string '42' to integer 42), injecting schema defaults, and pruning undeclared hallucinated fields. Use when enforcing schema compliance and type safety on structured LLM outputs before passing them to typed APIs; for healing unparseable raw JSON text with broken quotes or trailing commas, use heal_malformed_json instead. Read-only edge call (32 KB, 60 RPM): accepts 'raw_output' (with enum defaults), uses '?demo=true' or L402 (15 sats); HTTP 400 if invalid.",
      "inputSchema": {
        "properties": {
          "coercion_policy": {
            "description": "Policy for handling extra undeclared properties; defaults to 'prune_undeclared_and_coerce' when omitted.",
            "enum": [
              "prune_undeclared_and_coerce",
              "coerce_only_keep_extra",
              "strict_reject_on_mismatch"
            ],
            "type": "string"
          },
          "raw_output": {
            "description": "JSON string or object produced by an LLM to validate and coerce.",
            "type": "string"
          }
        },
        "required": [
          "raw_output"
        ],
        "type": "object"
      },
      "name": "enforce_json_schema_types"
    },
    {
      "description": "Extracts structured JSON records, named entities, dates, and financial figures from unstructured prose, emails, or HTML/Markdown dumps. Use when converting unstructured natural-language text into a structured JSON array of entities; for validating and coercing an existing JSON payload against a formal JSON Schema, use enforce_json_schema_types instead. Stateless read-only edge call (max 32 KB, 60 RPM): accepts 'text' ('extraction_target' defaults to 'general_entities_and_metrics'), supports '?demo=true' or L402 (25 sats), and returns HTTP 400 if invalid.",
      "inputSchema": {
        "properties": {
          "extraction_target": {
            "description": "Primary entity domain to extract; defaults to 'general_entities_and_metrics' when omitted.",
            "enum": [
              "general_entities_and_metrics",
              "contacts_and_organizations",
              "financial_line_items",
              "timeline_events"
            ],
            "type": "string"
          },
          "text": {
            "description": "Unstructured document, email body, or webpage text to extract structured entities from.",
            "type": "string"
          }
        },
        "required": [
          "text"
        ],
        "type": "object"
      },
      "name": "extract_structured_json_entities"
    },
    {
      "description": "Generates normalized high-dimensional semantic vector embeddings settled via Solana SPL USDC ('x402' protocol headers) for decentralized RAG pipelines. Use when computing text embeddings paid via Solana USDC x402 signatures rather than Bitcoin Lightning; for extracting discrete JSON entities from unstructured text, use extract_structured_json_entities instead. Stateless read-only edge call (max 32 KB, 60 RPM): accepts 'text' ('encoding_format' defaults to 'float_array'), supports '?demo=true' or x402 (10 sats), and returns HTTP 400 if invalid.",
      "inputSchema": {
        "properties": {
          "encoding_format": {
            "description": "Vector serialization format; defaults to 'float_array' when omitted.",
            "enum": [
              "float_array",
              "base64_float32"
            ],
            "type": "string"
          },
          "text": {
            "description": "Input text string or document chunk to convert into a normalized embedding vector.",
            "type": "string"
          }
        },
        "required": [
          "text"
        ],
        "type": "object"
      },
      "name": "generate_x402_vector_embeddings"
    },
    {
      "description": "Deterministic JSON syntax repair: normalizes quotes, unclosed brackets, and boolean literals into compliant RFC 8259 JSON structure without data deletion. Use when fixing syntax-broken JSON strings that fail JSON.parse(); for coercing field types and verifying properties on already-parseable JSON objects against a target JSON Schema, use enforce_json_schema_types instead. Read-only edge call (32 KB, 60 RPM): accepts 'malformed_json' (with enum defaults), uses '?demo=true' or L402 (15 sats); HTTP 400 if invalid.",
      "inputSchema": {
        "properties": {
          "malformed_json": {
            "description": "Raw broken, truncated, or markdown-wrapped JSON string to repair.",
            "type": "string"
          },
          "repair_mode": {
            "description": "Bracket-closing and literal-normalization strategy; defaults to 'heuristic_fast' when omitted.",
            "enum": [
              "heuristic_fast",
              "strict_rfc8259",
              "close_truncated_stream"
            ],
            "type": "string"
          }
        },
        "required": [
          "malformed_json"
        ],
        "type": "object"
      },
      "name": "heal_malformed_json"
    },
    {
      "description": "Initializes a Base USDC Machine Payments Protocol (MPP) metered inference session, allocating a streaming token quota and returning a step-by-step execution plan. Use when running session-metered agent planning settled via Base EVM MPP 'Payment-Challenge' headers; for mapping a task goal to L402 MCP tool calls without opening an MPP streaming session, use route_mcp_workflow_plan instead. Stateless read-only edge call (max 32 KB, 60 RPM): accepts 'task' ('token_quota_tier' defaults to 'compact_1024'), supports '?demo=true' or MPP (100 sats), and returns HTTP 400 if invalid.",
      "inputSchema": {
        "properties": {
          "task": {
            "description": "Agent workflow task to execute within the metered MPP session.",
            "type": "string"
          },
          "token_quota_tier": {
            "description": "Allocated streaming token ceiling for the session; defaults to 'standard_4096' when omitted.",
            "enum": [
              "compact_1024",
              "standard_4096",
              "extended_16384"
            ],
            "type": "string"
          }
        },
        "required": [
          "task"
        ],
        "type": "object"
      },
      "name": "infer_mpp_streaming_session"
    },
    {
      "description": "Maps a natural-language agent goal to an ordered sequence of Diogenes MCP tool calls with exact endpoint paths, JSON-RPC 2.0 payloads, and cumulative satoshi cost estimates. Use when an autonomous agent needs to plan which MCP tools to chain together to solve a multi-step objective within a satoshi budget; for compiling a static XML system prompt rather than an executable MCP tool call sequence, use compile_xml_agent_prompt instead. Read-only edge call (32 KB, 60 RPM): accepts 'objective' ('budget_preference' defaults to 'lowest_sats_deterministic'), uses '?demo=true' or L402 (20 sats); HTTP 400 if invalid.",
      "inputSchema": {
        "properties": {
          "budget_preference": {
            "description": "Cost vs depth routing trade-off; defaults to 'lowest_sats_deterministic' when omitted.",
            "enum": [
              "lowest_sats_deterministic",
              "max_verification_depth",
              "single_tool_fast"
            ],
            "type": "string"
          },
          "objective": {
            "description": "Natural-language description of the workflow goal to route across MCP tools.",
            "type": "string"
          }
        },
        "required": [
          "objective"
        ],
        "type": "object"
      },
      "name": "route_mcp_workflow_plan"
    },
    {
      "description": "Condenses long technical specifications, RFCs, research papers, or meeting transcripts into high-density executive bullet points preserving exact figures and decisions. Use when producing a human-readable executive summary of a single provided document; for researching a topic across the live web with Google Search citations, use synthesize_web_intelligence_brief instead. Stateless read-only edge call (max 32 KB, 60 RPM): accepts 'text' ('summary_format' defaults to 'executive_bullets'), supports '?demo=true' or L402 (50 sats), and returns HTTP 400 if invalid.",
      "inputSchema": {
        "properties": {
          "summary_format": {
            "description": "Output presentation structure; defaults to 'executive_bullets' when omitted.",
            "enum": [
              "executive_bullets",
              "decision_log_table",
              "one_paragraph_abstract"
            ],
            "type": "string"
          },
          "text": {
            "description": "Full document text, RFC, or transcript to summarize.",
            "type": "string"
          }
        },
        "required": [
          "text"
        ],
        "type": "object"
      },
      "name": "summarize_technical_document"
    },
    {
      "description": "Synthesizes PEP 8 compliant, self-contained Python 3 scripts, data parsers, and CLI utilities complete with type hints, docstrings, and inline assertion tests. Use when generating a standalone Python automation script or parser from a functional specification; for summarizing existing documentation or codebases into executive bullet points, use summarize_technical_document instead. Stateless read-only edge call (max 32 KB, 60 RPM): accepts 'prompt' ('dependency_policy' defaults to 'stdlib_only'), supports '?demo=true' or L402 (75 sats), and returns HTTP 400 if invalid.",
      "inputSchema": {
        "properties": {
          "dependency_policy": {
            "description": "Allowed library dependencies in generated code; defaults to 'stdlib_only' when omitted.",
            "enum": [
              "stdlib_only",
              "allow_requests_and_pydantic",
              "async_httpx_tokio"
            ],
            "type": "string"
          },
          "prompt": {
            "description": "Functional specification, input/output formats, and edge cases for the Python script.",
            "type": "string"
          }
        },
        "required": [
          "prompt"
        ],
        "type": "object"
      },
      "name": "synthesize_python_automation_code"
    },
    {
      "description": "Synthesizes linear-time O(n) regular expressions free of catastrophic backtracking (ReDoS) and validates them against positive and negative test strings. Use when generating and verifying a regular expression pattern for input validation or lexical parsing; for repairing broken JSON syntax directly without writing custom regexes, use heal_malformed_json instead. Stateless read-only edge call (max 32 KB, 60 RPM): accepts 'description' ('flavor' defaults to 'ecmascript_js'), supports '?demo=true' or L402 (15 sats), and returns HTTP 400 if invalid.",
      "inputSchema": {
        "properties": {
          "description": {
            "description": "Natural-language specification of the text pattern to match (e.g. 'RFC 5322 email addresses excluding plus-aliases').",
            "type": "string"
          },
          "flavor": {
            "description": "Target regular expression engine syntax; defaults to 'ecmascript_js' when omitted.",
            "enum": [
              "ecmascript_js",
              "python_re",
              "rust_regex_dfa",
              "pcre2"
            ],
            "type": "string"
          }
        },
        "required": [
          "description"
        ],
        "type": "object"
      },
      "name": "synthesize_safe_regex"
    },
    {
      "description": "Synthesizes a real-time market or technical intelligence brief grounded in live Google Search, returning strict JSON ('executive_summary', 'key_developments', 'sentiment', 'sources'). Use when fetching a structured JSON market/news brief with live web citations and sentiment classification; for summarizing caller-supplied text offline without live web search, use summarize_technical_document instead. Read-only edge call (32 KB, 60 RPM): accepts 'topic' ('focus' defaults to 'technical_and_market_catalysts'), supports '?demo=true' or L402 (75 sats), and returns HTTP 400 if invalid.",
      "inputSchema": {
        "properties": {
          "focus": {
            "description": "Analytical angle applied to the brief; defaults to 'technical_and_market_catalysts' when omitted.",
            "enum": [
              "technical_and_market_catalysts",
              "security_incidents",
              "regulatory_and_macro"
            ],
            "type": "string"
          },
          "topic": {
            "description": "Company, protocol, market sector, or technical topic to brief.",
            "type": "string"
          }
        },
        "required": [
          "topic"
        ],
        "type": "object"
      },
      "name": "synthesize_web_intelligence_brief"
    },
    {
      "description": "Audits a compacted conversation summary against the original full context to detect dropped user constraints, lost file paths, altered numbers, or missing pending tasks. Use when verifying that an LLM context-compaction summary retained all task-critical facts before truncating history; for generating a new executive summary from a raw technical document, use summarize_technical_document instead. Read-only edge call (32 KB, 60 RPM): accepts 'original_context' ('compacted_summary' defaults to 'standard'), supports '?demo=true' or L402 (15 sats), and returns HTTP 400 if invalid.",
      "inputSchema": {
        "properties": {
          "audit_focus": {
            "description": "Entity class prioritized during retention scoring; defaults to 'constraints_paths_and_numbers' when omitted.",
            "enum": [
              "constraints_paths_and_numbers",
              "code_identifiers_only",
              "chronological_decisions"
            ],
            "type": "string"
          },
          "compacted_summary": {
            "description": "Candidate compacted summary to audit for dropped constraints or entities.",
            "type": "string"
          },
          "original_context": {
            "description": "Full pre-compaction conversation history or task log.",
            "type": "string"
          }
        },
        "required": [
          "original_context",
          "compacted_summary"
        ],
        "type": "object"
      },
      "name": "verify_context_compaction_fidelity"
    }
  ]
}

What the leaf hashes

{"body":"35bc6db97b9ec6c8a06bc1a6a7c1557a4336abcb51bddb5e81ab23dee91edaf5","id":291577,"kind":"mcp.tools","method":"probe.mcp/1","observed_us":1793048174616828,"prev":null,"subject":"1f0225a3-d092-4dc2-8a61-b94a6030630a","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. 0x78da3606f47f692c7cc929dc82516c03ced01b7b5febe90f5aaca9ad89740ada
2. 0xff422ae98ac8aa81e46d84ce2a964eaaad355a6c28390d3c98318654284d03ca
3. 0xd3ce41c4d0e0b85a064dd3f50d5077aeb294f80359b3f22c000e710081f20f37
4. 0x616d73125c2e645de7101840040bf778ff525da867e2e8f205a1ad6af980a46a
5. 0xf1c405f09a5bf8cad211eaa106345633f69ae1ee0aee5f31aff2f7f7db889f12
6. 0x264a490c4e98fd2213fe85f76a38c2ea062852f08cb6ea3bebc132da46a85470
7. 0xe7db1ce215db38691387aab711d015e30f94d05100da6bc7d723b2d764107950
8. 0xc51e2c71e6a806b5455909fbfe72f5de8acae48348f8303349ea501dd3f7371b
9. 0x31a27592519587e4aaaa44f1316d701c567fcad8c919002311c954240ccbdecc
10. 0x97e46b70e0c331348eabcb4ab670fdafec5ed7123ffa5a6a8f2771a3eb69c9e9
11. 0x15ee99441401ec9e8c72aaf51bd6c821ec4e6b18bc91225370e5c19c4c8e8448
12. 0xdcba35a3346e9c4b9ae1267cc5995998c2c33da9ef658e955eaae1589ebbdcad
13. 0x01c95624b1f2385ef774ee258a9ae58a4df07a1bdaa829de84e1d5201f3f39c2
14. 0x7721d38652b6444b18e1193824e50490ade04851ce6221771f6e374c4527d80f
15. 0x15039087bf60ae9b06b756ec3cbc299760e59923a438bbd91e34d105186db0ed
16. 0x2b6e23e3624a1b66e1d2298cfbce99a93ce6b13d24a58112a71802d79c5f7a76
17. 0xd21942ae3214eff88e08ab04884fd4b11958b51af9a0741b7668e2d07a2c56c9
18. 0x9a9024edb5069d88a324facb310a5ce8e495214bbaa968f077bede437390194f
19. 0x9b9ce5b74b9bab5121612ef6b64920a8d453cbf311096a423eef9702747e868a

Day root

0xf43e8dcc3d451a4788b26be904c55e8c4c0fa75c9666796c51db38d37e2c0df8

Chained

0x702d5ac508f08b0d1accf36af25822d8e5660367494edd17a642fb61e158a57a

Published

On Arc Testnet, block 69251483: 0x495162f8936e19f0039918feebe6c307f327cd6bf8c37219620b1970041fa9ae