Optimize vendor contract terms
Optimize vendor contract terms across coherent usage, service-credit, exit, and fallback-price scenarios using exact option evaluation, CVaR, Pareto screening, and total-variation probability robustness.
What it's for
Lets technology and finance leaders compare real contract mechanics—not headline unit prices—and identify terms that remain attractive when usage probabilities are wrong and exit matters.
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.
| Field | Type | Role | Required |
|---|---|---|---|
| contract_options | array of objects (9 fields) ≥ 2 items | Evidence | Yes |
| discount_rate_per_period | number ≥ 0, ≤ 1 | Your calibration | Optional |
| max_detail_rows | integer ≥ 1, ≤ 500 | Numerical control | Optional |
| maximum_cvar_cost | number ≥ 0 | Your calibration | Optional |
| maximum_expected_cost | number ≥ 0 | Your calibration | Optional |
| maximum_term_periods | integer ≥ 1, ≤ 120 | Your calibration | Optional |
| probability_ambiguity_radius | number ≥ 0, ≤ 1 | Your calibration | Optional |
| risk_aversion | number ≥ 0 | Your calibration | Optional |
| scenarios | array of objects (6 fields) ≥ 2 items | Evidence | Yes |
| status_quo_option_id | string non-empty | Your calibration | Yes |
| tail_probability | number ≥ 0.001, ≤ 0.5 | Your calibration | Optional |
Each contract_options
record
| Field | Type | Required |
|---|---|---|
| committed_unit_price | number (≥ 0) | Yes |
| exit_fee | number (≥ 0) | Yes |
| fixed_fee_per_period | number (≥ 0) | Yes |
| id | string (non-empty) | Yes |
| minimum_units_per_period | number (≥ 0) | Yes |
| overage_unit_price | number (≥ 0) | Yes |
| setup_cost | number (≥ 0) | Yes |
| sla_credit_cap_per_period | number (≥ 0) | Yes |
| term_periods | integer (≥ 1, ≤ 120) | Yes |
{
"contract_options": [
{
"committed_unit_price": 2,
"exit_fee": 50,
"fixed_fee_per_period": 0,
"id": "status-quo",
"minimum_units_per_period": 100,
"overage_unit_price": 4,
"setup_cost": 0,
"sla_credit_cap_per_period": 0,
"term_periods": 2
},
{
"committed_unit_price": 2.2,
"exit_fee": 10,
"fixed_fee_per_period": 0,
"id": "flexible",
"minimum_units_per_period": 50,
"overage_unit_price": 3,
"setup_cost": 0,
"sla_credit_cap_per_period": 0,
"term_periods": 2
}
],
"probability_ambiguity_radius": 0.1,
"risk_aversion": 0.25,
"scenarios": [
{
"eligible_sla_credit_fraction_by_period": [
0,
0
],
"exit_period": -1,
"id": "high-usage",
"probability": 0.5,
"spot_unit_price_by_period": [
3,
3
],
"usage_by_period": [
200,
200
]Truncated for display — the full payload is 65 lines.
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": [
"Usage, fallback spot price, SLA-credit eligibility and exit timing are coherent joint scenarios over one contract horizon; probability weights, currency, tax, discounting, demand units and service definitions are comparable and contain the common shocks relevant to procurement.",
"Each candidate option fully specifies setup, term, minimum commitment, committed and overage prices, fixed fee, exit fee and enforceable SLA-credit cap; active-period commitment, post-term or post-exit fallback, credit limits and exit mechanics match the actual legal wording.",
"The robust objective moves up to the declared total-variation probability mass toward costly submitted scenarios and adds a governed multiple of the nominal CVaR premium; it cannot protect against omitted scenarios, counterparty failure, legal unenforceability, nonlinear tiers, tax, FX or service losses absent from the model.",
"Contract options and vendors are aggregate commercial choices, never named-person scores; the result is not legal, tax, procurement, credit or investment advice and does not authorize negotiation, signature, termination, migration or investigation without accountable review."
],
"configuration": {
"ambiguity_radius_is_total_variation_mass": true,
"discount_rate_per_period": 0,
"exact_exhaustive_option_evaluation": true,
"maximum_cvar_cost": null,
"maximum_expected_cost": null,
"maximum_term_periods": 2,
"probability_ambiguity_radius": 0.1,
"risk_aversion": 0.25,
"tail_probability": 0.1
},
"contract_option_diagnostics": [
{
"committed_unit_price": 2.2,
"cvar_discounted_cost": 1120,
"eligible": true,
"exit_fee": 10,
"expected_discounted_cost": 670,
"fixed_fee_per_period": 0,
"minimum_units_per_period": 50,
"option_id": "flexible",
"overage_unit_price": 3,
"pareto_efficient": true,
"passes_cvar_cost_gate": true,
"passes_expected_cost_gate": true,
"passes_term_gate": true,
"robust_risk_adjusted_cost": 872.5,
"selected": true,
"setup_cost": 0,
"sla_credit_cap_per_period": 0,
"tail_cost_premium": 450,
"term_periods": 2,
"worst_case_expected_discounted_cost": 760
},
{
"committed_unit_price": 2,
"cvar_discounted_cost": 1200,Truncated for display — the full payload is 103 lines.
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 Freeze coherent period scenarios for usage, fallback spot price, enforceable SLA-credit eligibility and exit timing, then enumerate legally feasible contract options with setup, term, minimum commitment, committed and overage price, fixed fee, exit fee and credit cap.
- 2 Apply active-contract commitment and bounded credits period by period, switch to spot cost after term or early exit, discount every scenario, and compute expected cost, CVaR, tail premium and Pareto efficiency.
- 3 Move the governed total-variation probability mass from cheaper toward costly submitted scenarios, minimize worst-case expected cost plus a tail premium under expected-cost, CVaR and lock-in limits, and report savings against the explicit status quo.
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.
Assumptions & guardrails
- Objectives use commensurable locally governed value units, constraints reflect real feasibility, and uncertainty covers plausible adverse inputs.
- Candidate terms match enforceable legal mechanics, scenario paths retain common demand, price, service and exit shocks, and fallback supply is genuinely available; all taxes, currencies, nonlinear tiers, credits, counterparty failure and switching consequences are complete.
- The recommendation is optimal only for its stated objective, feasible set, evidence, and solver guarantee; it is not a universal management optimum.
- Robustness protects only against probability error inside submitted scenarios, not omitted futures or invalid contracts; the selected option is decision support, not legal, tax, procurement, credit or signature authority.
Minimum evidence
- contract_options: at least 2 rows/items
- scenarios: at least 2 rows/items
- status_quo_option_id: required and organization-defined
How to validate it
Backtest the chosen action against simple feasible baselines on held-out scenarios, sweep costs/constraints/risk tolerance, and require constraint feasibility under adverse inputs.
Calibrating it to your org
Same for everyone
The mathematical kernel, validation rules, method version, and JSON output semantics are organization-independent; no tenant-trained coefficients or company benchmark is embedded in the function.
Specific to you
- finance- and legal-reconciled discounted scenario costs implementing the exact period-level tariff, credit, early-exit, post-term fallback, tax, currency, and renewal mechanics for every admissible option
- contract and counterfactual perimeter, scenario vintage and probabilities, horizon and discount basis, SLA evidence and enforceability, fallback availability, ambiguity radius, tail and risk preferences, expected/CVaR/term gates, legal review, and procurement authority
Calibration workflow
- 1 Define the management decision, target outcome, aggregate unit, privacy boundary, cadence, and prediction/intervention horizon for this organization.
- 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 Estimate statistical parameters on training history, but obtain costs, utilities, risk tolerance, practical-effect thresholds, capacity, and policy constraints from accountable decision owners.
- 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 Deploy only if the returned decision clears evidence, overlap, calibration, robustness, and guardrail checks; warning, unsupported, schema-gap, and fallback decisions are abstentions.
- 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": "optimize vendor contract terms across coherent" }
→ finds "optimize_vendor_contract_terms"
gitrevio_capability_describe
{ "capability_id": "optimize_vendor_contract_terms" }
→ returns the input schema and agent guidance shown on this page
gitrevio_capability_run
{ "capability_id": "optimize_vendor_contract_terms", "arguments": { ... } }
→ returns the result shown aboveWorks in Claude Desktop, Claude Code, Cursor, Cline, Continue.dev, Goose and Aider. See the MCP server.
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