Optimize engineering observability portfolio
Exactly select the budget-feasible metric and integration subset maximizing multivariate Gaussian information, then require held-out information retention with bootstrap uncertainty.
What it's for
Answers which engineering integrations and measurements are actually worth funding for the decisions leadership cares about.
What you give it
Every tool is a pure function: it computes from what you pass it and reads nothing on its own. Inputs split into evidence you supply from your connected systems, calibration your team owns, and numerical controls that affect precision but never the result's meaning.
| Field | Type | Role | Required |
|---|---|---|---|
| bootstrap_draws | integer ≥ 200, ≤ 20000 | Numerical control | Optional |
| budget | number > 0 | Your calibration | Yes |
| confidence_level | number ≥ 0.8, ≤ 0.99 | Your calibration | Optional |
| covariance_ridge | number ≥ 0.000001, ≤ 1 | Your calibration | Optional |
| max_detail_rows | integer ≥ 1, ≤ 500 | Numerical control | Optional |
| minimum_validation_information | number ≥ 0, ≤ 10 | Your calibration | Optional |
| minimum_validation_retention | number ≥ 0.1, ≤ 1 | Your calibration | Optional |
| observations | array of objects (4 fields) ≥ 100 items | Evidence | Yes |
| seed | integer | Numerical control | Optional |
| sensors | array of objects (2 fields) ≥ 2 items | Evidence | Yes |
| target_names | array of string ≥ 1 item | Evidence | Yes |
| validation_fraction | number ≥ 0.2, ≤ 0.5 | Your calibration | Optional |
Each observations
record
| Field | Type | Required |
|---|---|---|
| id | string (non-empty) | Yes |
| period | integer | Yes |
| signals | object | Yes |
| targets | object | Yes |
{
"bootstrap_draws": 200,
"budget": 2,
"observations": [
{
"id": "observability-0",
"period": 0,
"signals": {
"delivery_sensor": 0,
"noise": 0,
"redundant_delivery": 0.009900990099009901,
"reliability_sensor": 0
},
"targets": {
"delivery": 0,
"reliability": 0
}
},
{
"id": "observability-1",
"period": 1,
"signals": {
"delivery_sensor": 0.36633663366336633,
"noise": 0.17757009345794392,
"redundant_delivery": 0.37623762376237624,
"reliability_sensor": 0.4174757281553398
},
"targets": {
"delivery": 0.36633663366336633,
"reliability": 0.4174757281553398
}
},
{
"id": "observability-2",
"period": 2,
"signals": {
"delivery_sensor": 0.7326732673267327,
"noise": 0.35514018691588783,
"redundant_delivery": 0.7425742574257426,
"reliability_sensor": 0.8349514563106796
},
"targets": {
"delivery": 0.7326732673267327,
"reliability": 0.8349514563106796Truncated for display — the full payload is 1709 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": [
"Observations are ordered at a stable cadence, the final holdout represents future operation, and every candidate signal plus governed target is measured on the same aggregate row grain.",
"Gaussian mutual information captures linear and covariance dependence after standardization; nonlinear, delayed, or heavy-tailed information may require a different prespecified estimator.",
"The exact subset search optimizes training information under supplied comparable costs, while the held-out information interval and retention gate guard against selection optimism rather than eliminating it.",
"Selected sensors justify an instrumentation or data-integration portfolio for aggregate decisions; they do not establish causal drivers or authorize individual surveillance."
],
"decision": "observability_portfolio_validated",
"method": "exact_budgeted_gaussian_information_holdout_validation_v1",
"portfolio": {
"budget": 2,
"budget_remaining": 0,
"cost": 2,
"selected_sensor_ids": [
"delivery_sensor",
"reliability_sensor"
],
"selected_sensors": 2,
"training_information_nats": 2.372003,
"training_redundancy_nats": 0.003649,
"validation_gain_over_best_single_nats": 1.187461,
"validation_information_nats": 2.375204,
"validation_interval": [
2.329351,
2.375449
],
"validation_retention": 1.00135
},
"sample": {
"bootstrap_draws": 200,
"candidate_sensors": 4,
"confidence_level": 0.9,
"details_returned": 2,
"details_truncated": false,
"feasible_subsets_searched": 10,
"observations": 120,
"targets": 2,
"training_rows": 84,
"validation_rows": 36
},
"selected_sensor_details": [
{
"cost": 1,
"removal_marginal_information_nats": 1.184177,Truncated for display — the full payload is 55 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 Exactly select the budget-feasible metric and integration subset maximizing multivariate Gaussian information, then require held-out information retention with bootstrap uncertainty.
- 2 Evaluate the method-specific diagnostics and gates returned by the function, then abstain unless the declared decision clears them under locally governed thresholds.
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.
- The recommendation is optimal only for its stated objective, feasible set, evidence, and solver guarantee; it is not a universal management optimum.
Minimum evidence
- observations: at least 100 rows/items
- sensors: at least 2 rows/items
- target_names: at least 1 rows/items
- budget: 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
- aligned complete target and candidate-signal matrix
- chronological training/validation split
- annualized comparable integration or instrumentation costs
- measurement budget
- decision targets
- minimum held-out information and retention
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 20 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": "exactly select the budgetfeasible metric and" }
→ finds "optimize_engineering_observability_portfolio"
gitrevio_capability_describe
{ "capability_id": "optimize_engineering_observability_portfolio" }
→ returns the input schema and agent guidance shown on this page
gitrevio_capability_run
{ "capability_id": "optimize_engineering_observability_portfolio", "arguments": { ... } }
→ returns the result shown aboveWorks in Claude Desktop, Claude Code, Cursor, Cline, Continue.dev, Goose and Aider. See the MCP server.