Allocate budget with CVAR constraint

What it's for

Gives CEOs and investors a capital plan that explicitly caps severe downside instead of ranking initiatives only by optimistic expected ROI.

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.

{
  "budget": 80,
  "investments": [
    {
      "cost": 50,
      "id": "growth",
      "scenario_returns": [
        -120,
        20,
        100,
        180
      ]
    },
    {
      "cost": 30,
      "id": "resilience",
      "scenario_returns": [
        70,
        40,
        20,
        10
      ]
    },
    {
      "cost": 25,
      "id": "efficiency",
      "scenario_returns": [
        15,
        25,
        35,
        45
      ]
    }
  ],
  "maximum_cvar_loss": 10,
  "scenario_probabilities": [
    0.1,
    0.2,
    0.3,
    0.4
  ],
  "tail_probability": 0.1
}

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.

{
  "assumptions": [
    "Scenario returns are aligned joint worlds, not independently sampled marginals.",
    "CVaR is computed from finance-owned monetary loss at the declared tail probability.",
    "The no-investment portfolio is feasible; optimality is claimed only for exact enumeration."
  ],
  "decision": "cvar_constrained_allocation_supported",
  "method": "cvar_constrained_budget_allocation_v1",
  "selected_investments": [
    {
      "cost": 30,
      "expected_return": 25,
      "investment_id": "resilience",
      "standalone_cvar_loss": -10
    },
    {
      "cost": 25,
      "expected_return": 35,
      "investment_id": "efficiency",
      "standalone_cvar_loss": -15
    }
  ],
  "solver_diagnostics": {
    "candidate_portfolios_evaluated": 8,
    "feasible_candidate_count": 4,
    "optimality_proven": true,
    "portfolios_rejected_by_cvar": 3,
    "scenario_count": 4,
    "solver": "exact_enumeration"
  },
  "summary": {
    "budget_slack": 25,
    "expected_portfolio_return": 60,
    "maximum_cvar_loss": 10,
    "portfolio_cvar_loss": -55,
    "portfolio_var_loss": -55,
    "selected_count": 2,
    "selected_investment_ids": [
      "resilience",
      "efficiency"
    ],
    "tail_probability": 0.1,
    "total_cost": 55
  },

How it works

Constrained optimization — Pick the best feasible option under real limits — budget, headcount, dependencies, capacity — rather than ranking a list and hoping it fits.

1. 1 Freeze incremental investment costs and aligned joint scenario returns, normalize finance-owned probabilities, and define the loss tail and maximum acceptable CVaR before optimization.
2. 2 Evaluate budget-feasible portfolios, calculate discrete probability-weighted VaR and CVaR from joint portfolio returns, and reject every portfolio above the tail-loss ceiling.
3. 3 Choose the remaining portfolio with greatest expected return, retain cash when nothing is safe, and disclose whether enumeration proves optimality or a candidate heuristic was used.

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": "maximize expected portfolio return while keeping" }
  → finds "allocate_budget_with_cvar_constraint"

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

gitrevio_capability_run
  { "capability_id": "allocate_budget_with_cvar_constraint", "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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