Optimize license seat portfolio

What it's for

Converts license sprawl into an auditable purchase portfolio that balances committed discounts against overflow capacity, unused seats and business interruption.

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.

{
  "license_offers": [
    {
      "cost_per_pack": 450,
      "id": "annual-50",
      "maximum_packs": 3,
      "minimum_packs": 0,
      "pool_id": "engineering",
      "seats_per_pack": 50
    }
  ],
  "minimum_demand_coverage_probability": 0.95,
  "scenarios": [
    {
      "id": "base",
      "probability": 0.7
    },
    {
      "id": "growth",
      "probability": 0.3
    }
  ],
  "seat_pools": [
    {
      "id": "engineering",
      "on_demand_seat_capacity_scenarios": [
        100,
        100
      ],
      "on_demand_seat_cost_scenarios": [
        12,
        15
      ],
      "seat_demand_scenarios": [
        80,
        120
      ],
      "value_per_unserved_seat": 200
    }
  ]
}

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.

{
  "decision": "purchase_governed_license_seat_portfolio",
  "failed_gates": [],
  "guardrails": [
    "Seat demand must mean privacy-safe eligible aggregate usage under one product/feature definition and forecast vintage; named-user surveillance, presence, keystrokes, or commit volume are not license need.",
    "Pool-specific offers avoid double-counting shared entitlements; enterprise pools, transfer rights, minimums, true-ups, tiers, taxes, credits, and termination terms must match executable quotes.",
    "Unserved value is an organization-owned opportunity or continuity cost, not permission to force adoption or a score of employees who do or do not use a tool.",
    "Heuristic mode has no global certificate, and the output does not authorize purchase, renewal, cancellation, access removal, employee monitoring, or vendor action."
  ],
  "method": "stochastic_integer_license_seat_portfolio_v1",
  "scenario_diagnostics": [
    {
      "probability": 0.3,
      "scenario_id": "growth",
      "total_cost": 1200,
      "unserved_seats": 0,
      "unused_reserved_seats": 0
    },
    {
      "probability": 0.7,
      "scenario_id": "base",
      "total_cost": 900,
      "unserved_seats": 0,
      "unused_reserved_seats": 20
    }
  ],
  "selected_license_offers": [
    {
      "committed_cost": 900,
      "offer_id": "annual-50",
      "pack_count": 2,
      "pool_id": "engineering",
      "reserved_seats": 100
    }
  ],
  "solver": {
    "evaluated_plan_count": 4,
    "global_optimality_certificate": true,
    "maximum_exact_states": 100000,
    "method": "exact_integer_enumeration",
    "nominal_state_count": 4
  },
  "summary": {
    "committed_license_cost": 900,

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 Define privacy-safe aggregate entitlement pools, coherent demand and on-demand scenarios, business value of unserved demand, and executable pack offers with minimum and maximum quantities.
2. 2 Enumerate integer portfolios inside the declared state boundary or use deterministic forward addition, dispatch committed seats before bounded on-demand supply, and price commitment, overflow and denied demand.
3. 3 Select expected-plus-tail-risk cost subject to purchase budget and demand-coverage policy, and expose unused seats, unserved seats, solver coverage and bounded scenario evidence.

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": "choose integer license packs across aggregate" }
  → finds "optimize_license_seat_portfolio"

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

gitrevio_capability_run
  { "capability_id": "optimize_license_seat_portfolio", "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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