{
  "tool_id": "art-450-model-inventory-entry",
  "tool_version": "1.0.0",
  "display_name": "Model Inventory Entry Builder",
  "mcp_name": "build_model_inventory_entry",
  "mandate_type": "compliance_control",
  "wave": 73,
  "gpu": false,
  "url": "https://ainumbers.co/chaingraph/art-450-model-inventory-entry.html",
  "description": "Builds a single model-inventory record for a bank's SR 26-2 model-risk-management inventory: checks the caller-declared attributes (model name, owner, purpose, tier, development/deployment dates, last validation date) against the SR 26-2 required-field set, assigns a proportionality tier (limited/moderate/high) from caller-declared materiality and complexity inputs, and returns a completeness score plus a list of missing required fields. First node in the model-passport lifecycle (inventory entry, then outcome-analysis comparison in art-451, then validation status in art-453). Distinct from the shipped program-level gap analyzers (tools 339/451 SR 26-02 and SR 11-7 gap assessors), which score an institution's overall MRM program, not a single model's inventory record. Distinct from art-380 (AI workpaper record), which documents a completed assessment rather than registering a model. NaN-safe. Zero network, zero PII.",
  "input_schema_ref": "chaingraph/art-450-model-inventory-entry.html#manifest",
  "consumes": [],
  "feeds": ["art-451-model-outcome-analysis"],
  "status": "live",
  "conformance_fixtures": true,
  "compute_capability": "server",
  "compute_images": [
    {
      "system": "sha256-source",
      "image_id": "sha256:2de5269cd7ce2153d49d282c65e7561c5f097c2fc88f86daeb66ea327ad21a6e",
      "valid_from": "2026-07-23"
    },
    {
      "system": "risc0",
      "image_id": "sha256:a1a0bc89b5b1febaeda3519f6dbade0fa5ac16beeb143c4e1b01689573567bc6",
      "valid_from": "2026-07-23"
    }
  ],
  "export_capability": [
    "json"
  ],
  "compute_proof_ready": "ready",
  "compute_proof": {
    "type": "ZkVmReceipt",
    "system": "risc0",
    "receiptFormat": "groth16-bn254",
    "imageId": "sha256:a1a0bc89b5b1febaeda3519f6dbade0fa5ac16beeb143c4e1b01689573567bc6",
    "seal": "JGPs4o2VlA2lBQ3vhChaXVTvGEQrtkPcPQnD/EBJ1sUcItgbLqHzeCT8U0pR0wQOWFagAXX2CogLMS3YtmOpgAXr1sT9bH4uVC8y7ehos+LiXo38TJS2w6RSctEOuk1QCUz4haV1A3e2qh4EG02/2RqsxZ6pHYxwOq/Y7UbVQGEHzOC7ywGv1srzjXMvpaUweFk0Sld819BpViZZRPzEfSPkkRRX1MwPT2DxnErxnVvh1FyjaaoD5moy5zuz50ofBIGKIyNxOJ9x9Nw3CVN1UUqXl5x0o40eMsoPNP63k40C05NcewngUrsBKB9fUs3Pl65/PUsh0SZuO6ubDU1xjg==",
    "journal": {
      "chaingraph_version": "0.4.0",
      "kernel_digest": "sha256:2de5269cd7ce2153d49d282c65e7561c5f097c2fc88f86daeb66ea327ad21a6e",
      "output": {
        "ai_ml_model": true,
        "completeness_score": 100,
        "complexity_score": 3,
        "inventory_record": {
          "business_purpose": "Consumer credit approval scoring",
          "deployment_date": "2025-06-15",
          "development_date": "2025-03-01",
          "last_validation_date": "2026-01-10",
          "model_name": "Retail Credit Scoring Model v3",
          "model_owner": "Model Risk Management"
        },
        "materiality_score": 4,
        "missing_required_fields": [],
        "model_name": "Retail Credit Scoring Model v3",
        "third_party_vendor": false,
        "tier": "high",
        "tier_sum": 7,
        "usage_scope": "enterprise_wide"
      }
    }
  }
}