CausalLayer MCP

Submit an AI incident for deterministic causal liability attribution. Returns a signed CausalCertificate, per-agent liability allocation, evidence-chain completeness, regulatory mapping, and (where keys are configured) a Bitcoin-anchored proof. Cost: 50 credits. Three guardrails apply: PII scan, deterministic-only acknowledgement, and minimum evidence.

{
  "type": "object",
  "properties": {
    "title": {
      "type": "string",
      "minLength": 3
    },
    "description": {
      "type": "string"
    },
    "category": {
      "type": "string"
    },
    "severity": {
      "type": "string",
      "enum": [
        "low",
        "medium",
        "high",
        "critical"
      ]
    },
    "jurisdiction": {
      "type": "string"
    },
    "financial_impact_cents": {
      "anyOf": [
        {
          "type": "integer",
          "minimum": 0,
          "maximum": 9007199254740991
        },
        {
          "type": "null"
        }
      ]
    },
    "currency": {
      "type": "string",
      "minLength": 3,
      "maxLength": 3
    },
    "agents": {
      "minItems": 1,
      "type": "array",
      "items": {
        "type": "object",
        "properties": {
          "id": {
            "type": "string",
            "minLength": 1
          },
          "name": {
            "type": "string",
            "minLength": 1
          },
          "type": {
            "type": "string",
            "enum": [
              "ai_system",
              "human_operator",
              "vendor",
              "deployer",
              "user",
              "third_party"
            ]
          },
          "operator_role": {
            "type": "string",
            "enum": [
              "provider",
              "deployer",
              "user",
              "vendor",
              "regulator",
              "auditor"
            ]
          },
          "vendor_name": {
            "type": "string"
          },
          "model_id": {
            "type": "string"
          }
        },
        "required": [
          "id",
          "name",
          "type"
        ]
      }
    },
    "events": {
      "minItems": 1,
      "type": "array",
      "items": {
        "type": "object",
        "properties": {
          "id": {
            "type": "string",
            "minLength": 1
          },
          "type": {
            "type": "string",
            "minLength": 1
          },
          "timestamp": {
            "type": "string",
            "minLength": 1
          },
          "actor_id": {
            "type": "string"
          },
          "description": {
            "type": "string",
            "minLength": 1
          },
          "trace_id": {
            "type": "string",
            "pattern": "^[0-9a-f]{32}$"
          },
          "span_id": {
            "type": "string",
            "pattern": "^[0-9a-f]{16}$"
          },
          "trace_source": {
            "type": "string",
            "enum": [
              "opentelemetry",
              "jaeger",
              "zipkin",
              "datadog",
              "newrelic",
              "other"
            ]
          }
        },
        "required": [
          "id",
          "type",
          "timestamp",
          "description"
        ]
      }
    },
    "deterministic_only": {
      "type": "boolean",
      "const": true,
      "description": "G2: Must be true. Acknowledges CausalLayer is deterministic and not LLM-based."
    },
    "pii_acknowledged": {
      "default": false,
      "description": "G1: Set to true ONLY if caller has confirmed PII handling is permitted by their data agreement. False payloads with detected PII will be rejected.",
      "type": "boolean"
    }
  },
  "required": [
    "title",
    "agents",
    "events",
    "deterministic_only"
  ],
  "$schema": "http://json-schema.org/draft-07/schema#"
}

Independently verify a CausalCertificate end-to-end (signature, Merkle integrity, issuer status against the registry). Cost: 1 credit. In production env, certificates from non-active issuers are rejected.

{
  "type": "object",
  "properties": {
    "certificate": {
      "type": "object",
      "propertyNames": {
        "type": "string"
      },
      "additionalProperties": {},
      "description": "CausalCertificateV1 object as returned by submit_incident.certificate"
    }
  },
  "required": [
    "certificate"
  ],
  "$schema": "http://json-schema.org/draft-07/schema#"
}

Independently re-derive a CausalCertificate from its canonical input and compare byte-for-byte against the claimed certificate. This is the strongest verification path: it requires no trust in the issuer or signing key. Cost: 1 credit (same price as verify_certificate). Returns PASS only if every checked field (certificateId, request_hash, merkleRoot, verdict, causalGraph, fourFactorScoring, deviationTaxonomy, euRuleOverlay, cascadeAttenuation, damages, underwriting) matches identically.

{
  "type": "object",
  "properties": {
    "certificate": {
      "type": "object",
      "propertyNames": {
        "type": "string"
      },
      "additionalProperties": {},
      "description": "The CausalCertificate object claimed by the issuer."
    },
    "canonicalInput": {
      "type": "object",
      "propertyNames": {
        "type": "string"
      },
      "additionalProperties": {},
      "description": "The original incident body that produced the certificate — the same JSON originally posted to submit_incident or submit_otel_trace."
    }
  },
  "required": [
    "certificate",
    "canonicalInput"
  ],
  "$schema": "http://json-schema.org/draft-07/schema#"
}

Convert an OpenTelemetry OTLP JSON trace into a FaultKey incident and return the same deterministic CausalCertificate as submit_incident. Each span becomes an event; service.name groups spans into agents; W3C trace_id and span_id propagate as evidence pointers on the causal graph edges. Cost: 50 credits (same as submit_incident). Three guardrails apply: PII scan, deterministic-only acknowledgement, and minimum evidence (auto-satisfied when the trace has at least 1 span).

{
  "type": "object",
  "properties": {
    "title": {
      "type": "string",
      "minLength": 3
    },
    "otlp": {
      "type": "object",
      "propertyNames": {
        "type": "string"
      },
      "additionalProperties": {},
      "description": "OTLP JSON payload with resourceSpans[]. See https://opentelemetry.io/docs/specs/otlp/#json-protobuf-encoding"
    },
    "category": {
      "type": "string"
    },
    "jurisdiction": {
      "type": "string"
    },
    "financial_impact_cents": {
      "anyOf": [
        {
          "type": "integer",
          "minimum": 0,
          "maximum": 9007199254740991
        },
        {
          "type": "null"
        }
      ]
    },
    "currency": {
      "type": "string",
      "minLength": 3,
      "maxLength": 3
    },
    "deterministic_only": {
      "type": "boolean",
      "const": true,
      "description": "G2: Must be true. Acknowledges CausalLayer is deterministic."
    },
    "pii_acknowledged": {
      "default": false,
      "description": "G1: Set to true ONLY if PII handling is permitted by your data agreement. OTLP traces frequently leak user/session ids in attributes.",
      "type": "boolean"
    }
  },
  "required": [
    "title",
    "otlp",
    "deterministic_only"
  ],
  "$schema": "http://json-schema.org/draft-07/schema#"
}

Counterfactual remediation simulator. Given a certificate's verdict + fourFactorScoring + agents and a list of remediation IDs from the FK-METHOD-2026-003 catalog, return the apportioned shares each remediation would have produced (in isolation) and the composite shares if they all stack. Every remediation cites a specific statute or standard. GET /api/v2/remediation/catalog for the list of IDs. Cost: 1 credit (same price as verify_certificate). Pure deterministic; same inputs produce a byte-identical result.

{
  "type": "object",
  "properties": {
    "verdict": {
      "type": "object",
      "properties": {
        "primaryParty": {
          "type": "string"
        },
        "primaryShare": {
          "type": "number",
          "minimum": 0,
          "maximum": 1
        },
        "secondary": {
          "type": "array",
          "items": {
            "type": "object",
            "properties": {
              "party": {
                "type": "string"
              },
              "share": {
                "type": "number",
                "minimum": 0,
                "maximum": 1
              }
            },
            "required": [
              "party",
              "share"
            ]
          }
        }
      },
      "required": [
        "primaryParty",
        "primaryShare",
        "secondary"
      ],
      "description": "The verdict block from the CausalCertificate."
    },
    "fourFactorScoring": {
      "type": "object",
      "properties": {
        "primaryAgent": {
          "type": "string"
        },
        "causalProximity": {
          "type": "number",
          "minimum": 0,
          "maximum": 1
        },
        "behaviouralDeviation": {
          "type": "number",
          "minimum": 0,
          "maximum": 1
        },
        "controllability": {
          "type": "number",
          "minimum": 0,
          "maximum": 1
        },
        "regulatoryAlignment": {
          "type": "number",
          "minimum": 0,
          "maximum": 1
        },
        "weights": {
          "type": "object",
          "properties": {
            "causalProximity": {
              "type": "number"
            },
            "behaviouralDeviation": {
              "type": "number"
            },
            "controllability": {
              "type": "number"
            },
            "regulatoryAlignment": {
              "type": "number"
            }
          },
          "required": [
            "causalProximity",
            "behaviouralDeviation",
            "controllability",
            "regulatoryAlignment"
          ]
        }
      },
      "required": [
        "primaryAgent",
        "causalProximity",
        "behaviouralDeviation",
        "controllability",
        "regulatoryAlignment",
        "weights"
      ],
      "description": "The fourFactorScoring block from the CausalCertificate."
    },
    "agents": {
      "type": "array",
      "items": {
        "type": "object",
        "properties": {
          "id": {
            "type": "string"
          },
          "type": {
            "type": "string"
          }
        },
        "required": [
          "id"
        ]
      },
      "description": "Agent registry (id + type) so the simulator can map remediation targetType to specific party ids."
    },
    "remediations": {
      "minItems": 1,
      "type": "array",
      "items": {
        "type": "object",
        "properties": {
          "id": {
            "type": "string"
          },
          "appliedToParty": {
            "type": "string"
          }
        },
        "required": [
          "id"
        ]
      },
      "description": "List of remediation IDs from the catalog (e.g. vendor_adversarial_eval_suite, deployer_human_in_loop). Each may optionally pin appliedToParty to a specific agent id."
    }
  },
  "required": [
    "verdict",
    "fourFactorScoring",
    "agents",
    "remediations"
  ],
  "$schema": "http://json-schema.org/draft-07/schema#"
}

Multi-jurisdiction overlay (FK-METHOD-2026-004). Given a canonical attributable apportionment (party-id -> share), the union of all jurisdiction role tags on each actor, and the union of jurisdiction-specific flags, return side-by-side post-overlay shares for AU, EU, US, UK, CA (or a chosen subset) with the specific rules that fired in each, citation URLs, and a parties × jurisdictions matrix. v1 ships full implementations for AU and EU; US/UK/CA are research stubs marked `is_stub: true`. Use GET /api/v2/jurisdiction/catalog to discover support and stub status. Cost: 1 credit. Pure deterministic.

{
  "type": "object",
  "properties": {
    "attributable": {
      "type": "object",
      "propertyNames": {
        "type": "string"
      },
      "additionalProperties": {
        "type": "number",
        "minimum": 0,
        "maximum": 1
      },
      "description": "Canonical pre-overlay apportionment as { party_id: share }. Sum should approximate 1.0; the function renormalises within tolerance."
    },
    "actors": {
      "type": "array",
      "items": {
        "type": "object",
        "properties": {
          "id": {
            "type": "string"
          },
          "type": {
            "type": "string",
            "enum": [
              "ai_system",
              "vendor",
              "deployer",
              "human_operator",
              "user",
              "third_party"
            ]
          },
          "eu_chain_member": {
            "type": "array",
            "items": {
              "type": "string",
              "enum": [
                "manufacturer",
                "authorised_representative",
                "importer",
                "fulfilment_service_provider",
                "distributor",
                "online_platform_self_supplier",
                "substantial_modifier"
              ]
            }
          },
          "eu_resident": {
            "type": "boolean"
          },
          "apra_regulated": {
            "type": "boolean"
          },
          "acl_supplier": {
            "type": "boolean"
          },
          "unrecoverable": {
            "type": "boolean"
          }
        },
        "required": [
          "id",
          "type"
        ]
      },
      "description": "All actors with the union of jurisdiction-specific role tags. EU and AU tags coexist on the same actor record."
    },
    "flags": {
      "type": "object",
      "properties": {
        "high_risk_ai": {
          "type": "boolean"
        },
        "pld_compensable_damage": {
          "type": "boolean"
        },
        "deployer_used_contrary_to_instructions": {
          "type": "boolean"
        },
        "human_oversight_unassigned_or_unqualified": {
          "type": "boolean"
        },
        "human_oversight_nominally_assigned_not_present": {
          "type": "boolean"
        },
        "deployer_input_data_unrepresentative": {
          "type": "boolean"
        },
        "deployer_ignored_risk_signal": {
          "type": "boolean"
        },
        "deployer_failed_serious_incident_notification": {
          "type": "boolean"
        },
        "deployer_destroyed_logs": {
          "type": "boolean"
        },
        "deployer_employer_no_worker_notice": {
          "type": "boolean"
        },
        "deployer_public_authority_unregistered": {
          "type": "boolean"
        },
        "provider_failed_to_supply_instructions": {
          "type": "boolean"
        },
        "provider_breach_was_unforeseeable": {
          "type": "boolean"
        },
        "ai_is_opaque_black_box": {
          "type": "boolean"
        },
        "defendant_failed_disclosure_order": {
          "type": "boolean"
        },
        "substantial_modification_present": {
          "type": "boolean"
        },
        "substantial_modification_severity": {
          "type": "number",
          "minimum": 0,
          "maximum": 1
        },
        "acl_major_failure": {
          "type": "boolean"
        },
        "is_apra_regulated_service": {
          "type": "boolean"
        },
        "cps230_thirdparty_breach": {
          "type": "boolean"
        },
        "cps230_operational_breach": {
          "type": "boolean"
        },
        "vaiss_adherent": {
          "type": "boolean"
        },
        "vendor_no_docs": {
          "type": "boolean"
        }
      },
      "description": "Union of jurisdiction-specific flags. AI Act / PLD flags drive the EU overlay; ACL / CPS 230 / VAISS flags drive the AU overlay."
    },
    "jurisdictions": {
      "description": "Optional subset to compute. Defaults to all five.",
      "type": "array",
      "items": {
        "type": "string",
        "enum": [
          "AU",
          "EU",
          "US",
          "UK",
          "CA"
        ]
      }
    },
    "primaryJurisdiction": {
      "description": "Engine-level jurisdiction string (e.g. 'EU', 'DE', 'AU'). Used by the EU gate to decide engagement.",
      "type": "string"
    }
  },
  "required": [
    "attributable",
    "actors",
    "flags"
  ],
  "$schema": "http://json-schema.org/draft-07/schema#"
}

Deterministic prospective-evaluation gate (FK-METHOD-2026-006). Pass a ProposedAction BEFORE the agent delivers a response; receive one of three verdicts: 'allow', 'require_revision' (with specific factor-keyed directives), or 'block'. Uses the same four-factor engine that issues post-hoc certificates, so a single incident chains: prospective_pre_image -> response -> certificate -> anchor. This is a policy gate on structured action metadata, NOT a content safety classifier on raw prose. Thresholds are per-jurisdiction (EU strictest, US most permissive); read via GET /api/v2/gate/thresholds. Overrides are allowed but REQUIRE a governance rationale so the audit trail is complete. Cost: 1 credit. Pure deterministic.

{
  "type": "object",
  "properties": {
    "action": {
      "type": "object",
      "properties": {
        "action_id": {
          "type": "string",
          "description": "Stable id for this action; echoed back."
        },
        "action_type": {
          "type": "string",
          "enum": [
            "llm_response",
            "tool_call",
            "code_execution",
            "external_api_call",
            "human_handoff",
            "data_modification",
            "financial_transaction",
            "medical_advice",
            "legal_advice",
            "financial_advice",
            "content_moderation",
            "autonomous_decision",
            "other"
          ],
          "description": "The action category. Carries inherent regulatory weight."
        },
        "acting_agent_id": {
          "type": "string",
          "description": "Free-form id of the agent issuing the action."
        },
        "acting_agent_type": {
          "type": "string",
          "enum": [
            "ai_system",
            "vendor",
            "deployer",
            "operator",
            "human_user",
            "third_party"
          ],
          "description": "Liability-bias category of the acting agent."
        },
        "severity_estimate": {
          "type": "string",
          "enum": [
            "low",
            "medium",
            "high",
            "critical"
          ],
          "description": "The estimated severity if the action goes wrong."
        },
        "jurisdiction": {
          "description": "Jurisdiction overlay; defaults to AU.",
          "type": "string",
          "enum": [
            "AU",
            "EU",
            "US",
            "UK",
            "CA"
          ]
        },
        "cascade_depth": {
          "description": "How many upstream agents this action is downstream of. 0 = root; 3 = LLM->agent->tool->this. Applies cascade attenuation.",
          "type": "integer",
          "minimum": 0,
          "maximum": 9007199254740991
        },
        "eu_flags": {
          "description": "Optional EU AI Act flags; only used when jurisdiction === 'EU'.",
          "type": "object",
          "properties": {
            "high_risk_ai": {
              "type": "boolean"
            },
            "pld_compensable_damage": {
              "type": "boolean"
            },
            "human_oversight_unassigned_or_unqualified": {
              "type": "boolean"
            }
          }
        },
        "context_flags": {
          "description": "Context flags that inform the regulatoryAlignment and controllability sub-scores.",
          "type": "object",
          "properties": {
            "affects_vulnerable_population": {
              "type": "boolean"
            },
            "regulated_domain": {
              "type": "boolean"
            },
            "irreversible_if_executed": {
              "type": "boolean"
            },
            "human_in_the_loop_present": {
              "type": "boolean"
            }
          }
        },
        "upstream_incident_id": {
          "description": "Optional chain to an existing incident trace.",
          "type": "string"
        }
      },
      "required": [
        "action_id",
        "action_type",
        "acting_agent_id",
        "acting_agent_type",
        "severity_estimate"
      ],
      "description": "The structured ProposedAction to evaluate."
    },
    "overrides": {
      "description": "Optional per-call threshold override. Rationale REQUIRED for audit.",
      "type": "object",
      "properties": {
        "allow_below": {
          "type": "number",
          "minimum": 0,
          "maximum": 1
        },
        "block_at_or_above": {
          "type": "number",
          "minimum": 0,
          "maximum": 1
        },
        "rationale": {
          "type": "string",
          "description": "REQUIRED when overrides are provided. Cite the governance basis (e.g. 'ISO/IEC 42001 SoA §3.2 approval')."
        }
      },
      "required": [
        "rationale"
      ]
    }
  },
  "required": [
    "action"
  ],
  "$schema": "http://json-schema.org/draft-07/schema#"
}

Return the index of all CausalLayer Tessera anchor batches, or one batch's full JSON (signed Merkle root, leaves, OpenTimestamps proof reference). FREE.

{
  "type": "object",
  "properties": {
    "version": {
      "description": "Optional anchor version, e.g. '2026-05-16-v1.6.4-simulation-calibration'.",
      "type": "string"
    }
  },
  "$schema": "http://json-schema.org/draft-07/schema#"
}

Return the CausalLayer issuer registry, or one issuer record. The registry lists all trusted public-key fingerprints, key algorithms, validity windows, and the anchor-log repo for each active issuer. FREE — no API key required.

{
  "type": "object",
  "properties": {
    "issuer_id": {
      "description": "Optional issuer id, e.g. 'causallayer-prod-2026-q2'. If omitted, returns the full registry.",
      "type": "string"
    }
  },
  "$schema": "http://json-schema.org/draft-07/schema#"
}

Claude-powered structured extractor. Parses unstructured text (news articles, court filings, emails, PDFs, incident reports, logs) into the typed JSON schema required by submit_incident. Returns a ready-to-submit incident object with extracted agents, events, severity, jurisdiction, and financial impact. NOTE: This is a pre-processing convenience tool — the deterministic scoring engine itself remains LLM-free. Cost: 10 credits.

{
  "type": "object",
  "properties": {
    "text": {
      "type": "string",
      "minLength": 20,
      "maxLength": 50000,
      "description": "Unstructured text to extract from. Can be a news article, court filing, incident report, email, PDF text, log output, or any description of an AI incident."
    },
    "context_hint": {
      "description": "Optional hint about the source type (e.g., 'court filing', 'news article', 'internal incident report') to improve extraction accuracy.",
      "type": "string"
    },
    "jurisdiction_hint": {
      "description": "Optional ISO country code hint if the jurisdiction is known (e.g., 'AU', 'US', 'EU').",
      "type": "string"
    }
  },
  "required": [
    "text"
  ],
  "$schema": "http://json-schema.org/draft-07/schema#"
}