Allocate capacity by marginal value

What it's for

Answers where the next engineering-capacity unit creates the most portfolio value after setup cost, diminishing return, existing commitments, and downside are priced together.

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.

{
  "capacity_cost_per_unit": 50000,
  "initiatives": [
    {
      "fixed_activation_cost": 50000,
      "id": "platform",
      "marginal_value_scenarios": [
        [
          150000,
          220000,
          300000
        ],
        [
          80000,
          130000,
          180000
        ],
        [
          40000,
          70000,
          100000
        ]
      ],
      "minimum_capacity_units": 1,
      "required": true
    },
    {
      "fixed_activation_cost": 100000,
      "id": "growth",
      "marginal_value_scenarios": [
        [
          250000,
          400000,
          700000
        ],
        [
          180000,
          300000,
          500000
        ],
        [
          90000,
          150000,
          250000

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": [
    "Each row is an aggregate initiative with indivisible, fungible capacity increments; the expected gross marginal curve is nonincreasing, while aligned scenarios preserve portfolio dependence and may cross.",
    "Gross marginal value excludes the separately submitted fully loaded capacity-unit cost; fixed activation cost is charged once whenever an initiative receives positive capacity.",
    "Minimums are activation thresholds unless required is true, in which case they are hard commitments; omitted skills, sequencing, dependencies, switching costs, and delivery feasibility can change the allocation.",
    "Exact mode certifies only the submitted discrete model; heuristic mode has no global optimality certificate, and capacity units must never be translated automatically into named-person assignments or employment actions."
  ],
  "configuration": {
    "capacity_cost_per_unit": 50000,
    "initiative_count": 2,
    "maximum_exact_states": 262144,
    "represented_state_count": 9,
    "risk_aversion": 0.5,
    "scenario_count": 3,
    "solver_mode": "exact_enumeration",
    "state_count_is_capped": false,
    "tail_probability": 0.1
  },
  "decision": "marginal_value_capacity_allocation_optimized_exact",
  "initiative_allocation": [
    {
      "activated": true,
      "allocated_capacity_units": 3,
      "expected_gross_value": 964000,
      "expected_net_value": 714000,
      "fixed_activation_cost": 100000,
      "initiative_id": "growth",
      "maximum_capacity_units": 3,
      "minimum_capacity_units": 2,
      "next_increment_expected_net_value": null,
      "required": false
    },
    {
      "activated": true,
      "allocated_capacity_units": 1,
      "expected_gross_value": 230000,
      "expected_net_value": 130000,
      "fixed_activation_cost": 50000,
      "initiative_id": "platform",
      "maximum_capacity_units": 3,
      "minimum_capacity_units": 1,
      "next_increment_expected_net_value": 85000,
      "required": true
    }

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 fungible capacity units, each initiative's aligned gross marginal-value path, activation threshold, required minimum, fixed activation cost, and fully loaded capacity-unit cost.
2. 2 Evaluate whole-portfolio scenario value rather than summing standalone ROI, then choose integer allocations maximizing expected net value minus the governed CVaR-loss penalty within total capacity.
3. 3 Re-optimize after one additional unit for a lumpy shadow value, return unused capacity and next increments, and treat an infeasible required minimum or heuristic fallback as an explicit planning exception.

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": "allocate indivisible aggregate capacity across initiativespecific" }
  → finds "allocate_capacity_by_marginal_value"

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

gitrevio_capability_run
  { "capability_id": "allocate_capacity_by_marginal_value", "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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