TL;DR: MCP servers are emerging as a black box inside Kubernetes because many of them expose no metrics, structured logs, or traceable signals, according to Stacklok. That leaves observability stacks blind to AI tool-use paths, slows incident triage, and turns telemetry gaps into governance gaps for NHI and agentic AI operations.
At a glance
What this is: MCP servers inside Kubernetes can operate as black boxes because they often expose no metrics, structured logs, or traceable signals.
Why it matters: That matters because teams cannot reliably attribute AI tool-use, isolate failures, or govern non-human access paths when the control plane is invisible.
Context
Kubernetes observability depends on being able to see what each workload is doing, when it is doing it, and how that behaviour changes under load. MCP servers complicate that model because they can handle AI tool requests and state without exposing the telemetry patterns that Prometheus and OpenTelemetry expect.
The governance issue is not just operational visibility. When a service that mediates AI actions has weak telemetry, incident response, accountability, and non-human identity oversight all lose precision. In practice, that creates the same kind of blind spot that often appears when ephemeral workloads are treated as if they will leave a durable audit trail.
Key questions
Q: What breaks when MCP servers do not expose telemetry in Kubernetes?
A: Teams lose the ability to trace tool use, identify which service handled a request, and distinguish normal automation from risky behaviour. That undermines incident response, performance analysis, and governance at the same time. In practice, an unobservable MCP server becomes a black box that hides both operational faults and privilege misuse.
Q: Why do MCP servers create governance problems for AI workloads?
A: Because they mediate tool access for AI systems while often leaving little trace of what happened inside the interaction. That makes it harder to prove safe use, investigate failures, or confirm that permissions stayed within intended bounds. For security teams, the lack of evidence is itself a control weakness.
Q: How do security teams decide when an MCP deployment is production-ready?
A: A deployment is production-ready when identity is centralized, access is group-scoped, tool calls are logged, revocation is tested, and sensitive data is filtered before it reaches model context. If those controls are missing, the system may be functional but it is not yet governable.
Q: What is the difference between monitoring Kubernetes workloads and monitoring MCP tool use?
A: Workload monitoring tells you whether a pod is healthy, responsive, or resource constrained. MCP tool-use monitoring tells you which AI-mediated action was invoked, which downstream tool was called, and how to reconstruct the sequence when behaviour needs investigation.
Technical breakdown
Why MCP servers become black boxes in Kubernetes
Kubernetes observability assumes workloads can expose signals that external tools can scrape or collect. MCP servers often break that assumption because they are designed for minimal dependencies, fast startup, and lightweight tool mediation rather than full telemetry. If a server has no /metrics endpoint, emits unstructured logs, and cannot be traced, then the observability stack can only infer behaviour indirectly. In distributed systems, that is a weak control because the failure may sit in the AI model, the MCP server, or an upstream dependency. The problem is architectural, not just operational.
Practical implication: Treat telemetry coverage as a design requirement for every MCP server, not an afterthought added during incident response.
How Prometheus and OpenTelemetry complement each other
Prometheus and OpenTelemetry solve different parts of the visibility problem. Prometheus is strong on scrape-based metrics, alerting, and time-series analysis in dynamic Kubernetes environments. OpenTelemetry is stronger on cross-signal correlation, especially when traces, logs, and metrics need to be connected across service boundaries. Together they create a fuller picture of request flow and service health. But that combined model still depends on each workload participating in telemetry. If the MCP layer stays silent, the stack can monitor everything around it while still missing the actual AI tool-use path.
Practical implication: Instrument MCP services so they participate in both metric collection and trace propagation before they are allowed into production clusters.
What telemetry gaps mean for NHI and agentic AI governance
MCP servers are not just another Kubernetes workload because they sit in the path of AI actions and delegated tool use. That makes them part of the control surface for agentic AI identity and non-human access governance. If teams cannot see which server handled which request, they cannot confidently answer who or what initiated a tool action, what state changed, or whether a behaviour was expected. This is the observability gap turning into a governance gap: visibility is a prerequisite for accountability when machine-driven systems act at runtime.
Practical implication: Map MCP telemetry requirements to NHI governance so every tool-mediated action leaves an auditable trail.
NHI Mgmt Group analysis
MCP telemetry gaps are now an identity governance problem, not just an observability problem. When an MCP server mediates AI tool use, it becomes part of the runtime control path for a non-human actor. If that layer is invisible, teams cannot reliably reconstruct delegated access, tool invocation, or state changes. The practitioner conclusion is simple: telemetry policy must be written as part of machine identity governance, not left to platform teams alone.
Telemetry black box: this is the governance failure mode MCP introduces when observability is optional. Kubernetes operators are used to compensating for ephemeral workloads with metrics and traces, but an MCP server that emits neither creates a durable blind spot exactly where AI action needs auditability. That weakens incident triage, access review, and behavioural baselining at the same time. Practitioners should treat missing telemetry as a control deficiency, not a tooling inconvenience.
Prometheus and OpenTelemetry are necessary but not sufficient for AI tool mediation. They can expose health and performance, but they cannot create visibility where the application refuses to emit the right signals. For NHI and agentic AI programmes, that means the governance question shifts from which monitoring stack is deployed to whether the workload is instrumentable at all. The practical conclusion is to gate production approval on observable AI action paths.
The next observability gap is really a trust gap in distributed AI operations. Teams can monitor the cluster and still fail to monitor the AI-mediated control path inside it. That matters because governance frameworks assume action can be attributed, reviewed, and investigated after the fact. If the MCP layer is opaque, those assumptions weaken. The practitioner conclusion is to align observability requirements with audit and access control expectations before scaling MCP adoption.
From our research library:
- 24,008 unique secrets were exposed in MCP configuration files in 2025 alone, the protocol's first year of widespread adoption, according to the State of Secrets Sprawl 2026.
- Red Hat’s Kubernetes Security Report found that nearly 9 in 10 organizations had at least 1 container or Kubernetes security incident in the last 12 months.
- Read next: MCP Security Guide
What this signals
Telemetry blind spots become governance blind spots when AI systems rely on MCP to reach tools and state. Kubernetes teams can still watch the cluster while missing the non-human action path that matters most, which means observability standards need to extend into delegated AI operations. The control question is no longer only whether the workload is healthy, but whether every AI-driven action can be attributed and reviewed.
MCP servers force a change in how platform teams think about instrumentation boundaries. The cluster may be observable at the node, pod, and service levels, yet the AI mediation layer can remain opaque unless telemetry is required by policy. That makes instrumentability part of deployment governance, especially where non-human identities are acting through the workload.
MCP servers are already producing 24,008 unique secrets were exposed in MCP configuration files in 2025 alone, the protocol's first year of widespread adoption according to the State of Secrets Sprawl 2026. That reinforces the broader point that emerging AI control planes accumulate both visibility and credential risk quickly, so observability and secret governance need to be planned together.
For practitioners
- Define telemetry requirements for MCP servers Require every MCP workload to expose metrics, structured logs, and trace context before it is approved for production use. Make observability a deployment criterion, not an optional enhancement.
- Instrument AI tool-use paths end to end Capture request lineage from the AI system into the MCP server and out to downstream tools so responders can separate model behaviour from server behaviour and external dependency issues.
- Set governance gates for invisible workloads Block clusters from accepting MCP services that cannot be discovered, monitored, and audited by existing telemetry pipelines, especially where delegated AI actions are in scope.
- Baseline Kubernetes observability by workload type Document which services must expose scrapeable metrics, which need trace correlation, and which require structured event logging so gaps are visible before incidents occur.
Key takeaways
- MCP servers can create an observability gap inside Kubernetes because they often sit in the AI tool path without exposing the metrics, logs, or traces teams need.
- That gap is operational, but it is also a governance issue because delegated machine actions become harder to attribute, review, and investigate.
- Production approval for MCP workloads should depend on whether the service can participate in the existing telemetry model, not simply on whether it starts successfully.
Standards & Framework Alignment
This section maps relevant standards and security frameworks to the operational risks and controls described in this guidance.
OWASP Non-Human Identity Top 10 and MITRE ATT&CK address the attack and risk surface, while NIST CSF 2.0 sets the governance and control requirements practitioners need to meet.
| Framework | Control / Reference | Relevance |
|---|---|---|
| OWASP Non-Human Identity Top 10 | NHI-02 — Secret Leakage | MCP servers in the article are tightly linked to secret exposure in configuration files and runtime paths. |
| NHI-10 — Human Use of NHI | MCP servers mediate non-human actions that must be attributable to the human or system requesting them. | |
| Recommendation — Scan MCP configurations and runtime artefacts for leaked secrets and revoke exposed credentials immediately. Map human-initiated AI tool use to accountable NHI workflows and require audit trails for delegated actions. | ||
| MITRE ATT&CK | TA0007;TA0009 — Discovery; Collection | The article is about inability to discover and trace MCP actions across a distributed environment. |
| Recommendation — Map missing telemetry to discovery and collection gaps, then prioritize instrumentation where requests cannot be traced. | ||
| NIST CSF 2.0 | PR.DS-01 — Data-at-rest is protected | The post highlights configuration exposure and the need to protect operational data and secrets used by MCP services. |
| DE.CM-01 — Networks and network services are monitored to detect potential cybersecurity events | The core issue is insufficient monitoring coverage for MCP services inside Kubernetes. | |
| Recommendation — Apply PR.DS-01 to protect MCP configuration data and any embedded credentials from exposure. Use DE.CM-01 to ensure MCP traffic and service behaviour are monitored by the platform's detection stack. | ||
Key terms
- Model Context Protocol: Model Context Protocol is an open protocol that lets AI agents connect to tools and data sources. It expands what an agent can reach, so governance has to cover not only the model and its prompts, but also every system that can receive or return agent-driven data.
- Runtime Observability Gap: The disconnect between what identity systems think was granted and what access systems show was actually used. This gap weakens governance because teams cannot confidently decide whether access is still necessary, especially in hybrid and distributed environments.
- Telemetry correlation: The process of joining separate security and application signals into one timeline so analysts can interpret them together. For identity work, this means linking sign-in risk with downstream SaaS activity to decide whether an event is suspicious, confirmed, or benign.
- Non-Human Identity (NHI): A digital identity assigned to a non-human entity such as a software application, service account, API key, bot, machine, or AI agent that enables it to authenticate and interact with systems without direct human involvement. NHIs now outnumber human identities in most enterprises by 25 to 50 times.
Deepen your knowledge
The NHI Foundation Level course, the industry's only accredited NHI security programme, covers NHI governance, workload identity, and the controls that help security teams govern machine-driven access. It is designed for practitioners who need to connect identity oversight to real operational environments.
Published by the NHIMG editorial team on August 20, 2026.
Updated on October 12, 2026.
NHI Mgmt Group, the independent authority on Non-Human Identity, IAM, and Agentic AI security. nhimg.org