Analyze coordination entropy
Quantify privacy-safe cross-team seam complexity, concentration, latency, and failure load.
What it's for
Measures the seams between teams — how complex, concentrated, slow and failure-prone the hand-offs are — from aggregate signals, without profiling individuals.
Deepens cross-team dependencies and context-switching claims at organization level.
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 |
|---|---|---|---|
| interactions | array of objects (5 fields) | Evidence | Yes |
| min_unit_size | integer ≥ 2 | Your calibration | Optional |
| unit_sizes | object | Evidence | Yes |
Each interactions
record
| Field | Type | Required |
|---|---|---|
| failure_rate | number (≥ 0, ≤ 1) | Optional |
| from_unit | string (non-empty) | Yes |
| latency_hours | number (≥ 0) | Optional |
| to_unit | string (non-empty) | Yes |
| volume | number (≥ 0) | Yes |
{
"interactions": [
{
"failure_rate": 0.12,
"from_unit": "checkout",
"latency_hours": 18,
"to_unit": "platform",
"volume": 42
},
{
"failure_rate": 0.05,
"from_unit": "checkout",
"latency_hours": 8,
"to_unit": "data",
"volume": 17
}
],
"min_unit_size": 5,
"unit_sizes": {
"checkout": 8,
"data": 7,
"platform": 11
}
}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.
{
"coordination_load_index": 98,
"cross_boundary_share": 1,
"effective_seam_count": 1.82,
"interpretation": "High entropy means coordination is spread across many seams; high concentration means one seam is a single point of dependency.",
"largest_seam_concentration": 0.7119,
"method": "coordination_entropy_v1",
"normalized_seam_entropy": 0.8663,
"privacy": {
"input_volume": 59,
"min_unit_size": 5,
"suppressed_edges": 0,
"suppressed_volume": 0
},
"top_seams": [
{
"failure_rate": 0.12,
"from_unit": "checkout",
"latency_hours": 18,
"share_of_cross_team_work": 0.7119,
"to_unit": "platform",
"volume": 42
},
{
"failure_rate": 0.05,
"from_unit": "checkout",
"latency_hours": 8,
"share_of_cross_team_work": 0.2881,
"to_unit": "data",
"volume": 17
}
],
"units": [
{
"destination_count": 2,
"normalized_entropy": 0.8663,
"outbound_volume": 59,
"unit": "checkout"
}
],
"weighted_failure_rate": 0.0998,
"weighted_latency_hours": 15.12
}How it works
Decision analysis — Turn uncertainty, cost and risk appetite into a defensible choice, with the reasoning left inspectable.
- 1 Quantify privacy-safe cross-team seam complexity, concentration, latency, and failure load.
- 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
- Actions, outcomes, utilities, evidence boundaries, uncertainty representation, and accountable ownership match the actual decision.
- The result structures a governed choice; it does not replace accountable judgment or authorize action outside the declared decision boundary.
Minimum evidence
- interactions: required and organization-defined
- unit_sizes: required and organization-defined
How to validate it
Validate on future periods or held-out aggregate units, compare with a simple baseline, and require stability across plausible metric definitions and decision thresholds.
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
- metric/outcome definitions, entity grain, observation window, costs, thresholds, priors, and constraints represented by the function inputs
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": "quantify privacysafe crossteam seam complexity concentration" }
→ finds "analyze_coordination_entropy"
gitrevio_capability_describe
{ "capability_id": "analyze_coordination_entropy" }
→ returns the input schema and agent guidance shown on this page
gitrevio_capability_run
{ "capability_id": "analyze_coordination_entropy", "arguments": { ... } }
→ returns the result shown aboveWorks in Claude Desktop, Claude Code, Cursor, Cline, Continue.dev, Goose and Aider. See the MCP server.