What Does an Observability Forecasting Foundation Model Know?

Time-series foundation models (TSFMs) are increasingly deployed as zero-shot forecasters in observability platforms, yet the operational concepts encoded within their internal representations remain largely un-audited. To address this gap, we introduce $\texttt{toto-interp}$, a control-first interpretability protocol that pairs linear probes with four rigorous baselines: raw-feature, shuffled-label, randomized-backbone, and supervised raw-window Fourier Neural Operator (FNO) controls. Applying this harness to TOTO on BOOM, we demonstrate that three structural telemetry concepts, such as cadence bucket ("frequency"), metric type, and domain, are linearly decodable, layer-localized, and specifically tied to pretraining. A budget-matched replication on MOMENT-base successfully recovers these same axes, confirming these representations are not isolated artifacts of the TOTO architecture. Furthermore, our matched-null and on-manifold interventions reveal a critical two-way dissociation between linear decodability and forecast-sensitivity. For instance, concepts like future burstiness are highly decodable yet completely inert under intervention, whereas other weakly decodable directions can severely disrupt forecasts off-manifold. By releasing the audit harness and detailing negative control cases (such as cardinality and shift risk), we demonstrate that linear decodability alone is insufficient for reliable post-training steering or deployment auditing in TSFMs.