Calculate analytics calibration liability

What it's for

Shows CEOs, CTOs and investors the expected and severe-loss exposure hidden in unmaintained analytics, and whether the organization has enough maintenance reserve.

What you give it

Inputs split into evidence read from your connected systems, calibration your team owns, and numerical controls that affect precision but never the result's meaning.

{
  "current_maintenance_reserve": 50,
  "functions": [
    {
      "decision_value_at_risk_scenarios": [
        100,
        100,
        100
      ],
      "id": "completion-forecast",
      "loss_fraction_if_uncalibrated_scenarios": [
        0.5,
        0.5,
        0.5
      ],
      "remediation_cost_scenarios": [
        10,
        10,
        10
      ],
      "uncalibrated_state_scenarios": [
        false,
        true,
        true
      ]
    },
    {
      "decision_value_at_risk_scenarios": [
        200,
        200,
        200
      ],
      "id": "decision-policy",
      "loss_fraction_if_uncalibrated_scenarios": [
        0.25,
        0.25,
        0.25
      ],
      "remediation_cost_scenarios": [
        20,
        20,
        20
      ],
      "uncalibrated_state_scenarios": [

What you get back

This is the actual output of running the example above — computed by the same function the platform calls, not an illustration.

{
  "configuration": {
    "maximum_acceptable_cvar_loss": null,
    "reserve_confidence_level": 0.9,
    "tail_probability": 0.2
  },
  "decision": "analytics_maintenance_liability_action_required",
  "failed_gates": [
    "maintenance_reserve_shortfall"
  ],
  "function_liability_contributions": [
    {
      "expected_liability": 14,
      "function_id": "decision-policy",
      "share_of_expected_liability": 0.3182,
      "tail_cvar_contribution": 70
    },
    {
      "expected_liability": 30,
      "function_id": "completion-forecast",
      "share_of_expected_liability": 0.6818,
      "tail_cvar_contribution": 60
    }
  ],
  "guardrails": [
    "Scenario columns must be coherent joint states: independently sorting or multiplying marginal failure probabilities destroys the dependence that drives portfolio tail liability.",
    "Decision value at risk, loss fractions, remediation cost, currency, horizon and reserve confidence are finance/risk-owned. Activity volume is not value exposure and missing common-cause scenarios remain outside CVaR.",
    "Function contribution allocates modeled portfolio loss for maintenance planning. It is not employee blame, causal attribution or an automatic suspension decision."
  ],
  "method": "coherent_calibration_failure_scenario_liability_and_tail_allocation_v1",
  "summary": {
    "current_maintenance_reserve": 50,
    "expected_calibration_liability": 44,
    "function_count": 2,
    "maintenance_reserve_shortfall": 80,
    "required_maintenance_reserve": 130,
    "reserve_breach_probability": 0.5,
    "scenario_count": 3,
    "tail_conditional_value_at_risk_loss": 130,
    "value_at_risk_loss": 130
  },
  "truncation": {
    "functions_omitted": 0
  }

How it works

Simulation & stress testing — Run the system forward many times to see what the bad tail actually looks like.

1. 1 Freeze complete joint scenarios describing which function versions are uncalibrated together, plus finance-owned decision exposure, loss-given-failure and remediation cost on one horizon and currency basis.
2. 2 Build function and portfolio loss in each unchanged joint scenario, aggregate expected liability, weighted VaR/CVaR and exact function tail contributions without independently sorting marginal outcomes.
3. 3 Compare the governed confidence reserve and optional CVaR limit with current maintenance reserve, expose breach probability and quantify the shortfall requiring action.

Before you trust it

Every tool in the catalog ships with the conditions under which its answer is meaningful — and the conditions under which it should abstain instead of guessing.

Calibrating it to your org

Calibration workflow

1. 1 Define the management decision, target outcome, aggregate unit, privacy boundary, cadence, and prediction/intervention horizon for this organization.
2. 2 Build a tenant-scoped historical cohort using only information available before each prediction or decision; preserve zero periods, censoring, assignment probabilities, and unresolved outcomes when the method requires them.
3. 3 Estimate statistical parameters on training history, but obtain costs, utilities, risk tolerance, practical-effect thresholds, capacity, and policy constraints from accountable decision owners.
4. 4 Validate on later time windows or held-out aggregate units at the deployment grain, against a simple baseline and the function-specific validation strategy.
5. 5 Deploy only if the returned decision clears evidence, overlap, calibration, robustness, and guardrail checks; warning, unsupported, schema-gap, and fallback decisions are abstentions.
6. 6 Monitor realized outcomes, data drift, coverage, and decision regret; recalibrate at a governed cadence or after a detected regime/definition change, never merely because a stakeholder dislikes the result.

Call it from your AI

You don't wire up 388 tools in your MCP client. The GitRevio MCP server exposes 18 tools, three of which let an agent search the catalog, read a tool's schema, and run it — so the assistant finds this one on its own.

gitrevio_capabilities_search
  { "q": "price the hidden financial liability of" }
  → finds "calculate_analytics_calibration_liability"

gitrevio_capability_describe
  { "capability_id": "calculate_analytics_calibration_liability" }
  → returns the input schema and agent guidance shown on this page

gitrevio_capability_run
  { "capability_id": "calculate_analytics_calibration_liability", "arguments": { ... } }
  → returns the result shown above

Works in Claude Desktop, Claude Code, Cursor, Cline, Continue.dev, Goose and Aider. See the MCP server.

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