Source-backed documentation update: API metrics interval diagnostics — 2026-10-13

User request: review the repository code and improve documentation. Branch starting revision: main@a0cd473de57059ca376e8fb88fb693bdbe0d1a23. Review mode: AI-assisted inspection via connected GitHub APIs, not a full-file security review or an independently executed runtime test.

What was checked

Inspected the repository README, agent/contribution instructions, documentation and architecture indexes, CLI and HTTP references, the existing executable/module catalogs, open PRs, the code behind the Control Tower metrics route (idkmesh/control_tower_ui.py), the counter implementation (idkmesh/api_observability.py), the newly merged interval function (idkmesh/otel_metrics.py), and tests/test_api_observability_intervals.py. The newer interval evaluator was not explained in the existing operator-facing guides.

What was changed

Added a source-backed metrics interval guide with a self-contained, dependency-free in-memory example, fixed-bucket interpretation, a distinction between HTTP snapshot collection and trusted process epochs, and the eight current UNKNOWN reasons. Added tests/test_api_metrics_interval_docs.py to execute the example and check that every source-defined UNKNOWN reason is explained. Updated the observability specification, API quickstart, documentation index, and troubleshooting help to link the guide and clarify authority.

Evidence and limitations

Reviewed source-level definitions, HTTP handler, signatures, and existing tests on the stated branch baseline.
This is a documentation-and-test addition, not implementation of collector identity, 5-minute rolling SLOs, external OTLP export, alerts, new HTTP routes, or changes to capability evidence levels.
Local pytest was not run. The available execution environment cannot resolve GitHub to clone the repository. Exact-head PR CI and the focused python -m pytest -q tests/test_api_metrics_interval_docs.py tests/test_api_observability_intervals.py are required before merge.
No independent human or production review is claimed. AI-assisted source reading and authoring were used.

Community impact: new users can understand raw service counters and avoid incorrectly treating a restart, invalid snapshot, or empty interval as a measured zero-error service.

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