The Tool Desk
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Groundcover provides two relevant paths: its MCP Server connects an AI assistant to live telemetry, while Agent Mode supports open-ended investigations inside the customer cloud. Both can accelerate diagnosis, but neither removes the need for least privilege, sandboxing, prompt-injection defenses and an audit trail of prompts, tool calls, results, approvals and network decisions.
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What a safe Groundcover investigation looks like
A production investigation should be a staged, reversible process. The agent gathers evidence first, explains what the evidence supports, proposes a change second and waits for a person before producing any side effect.
- Define scope: name the service, namespace, cluster, environment and time window.
- Detect and correlate: inspect the relevant SLO, then correlate logs, traces, metrics and Kubernetes events.
- Compare changes: check recent deployments, configuration changes and dependency behavior within the same window.
- Form a hypothesis: state the likely cause, the evidence for it, competing explanations and confidence.
- Propose a reversible fix: describe the smallest change, expected effect, rollback method and validation signal.
- Obtain approval: pause before any write, deployment, rollback, ticket mutation, pull request, merge or external communication.
- Validate and record: measure the same SLO and telemetry after the approved action and retain the complete decision trail.
This separation matters because a model can summarize evidence well while still being unsafe to trust with irreversible authority. Anthropic’s security guidance states that “model behavior is not a security control.” Treat the model as an analyst operating inside technical controls, not as the control itself.
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Choose the connection model
Groundcover describes two ways to let an AI assistant investigate incidents. They can coexist, but they have different operational boundaries.
| Capability | Groundcover MCP Server | Groundcover Agent Mode |
|---|---|---|
| Primary use | Connect Claude Code and other AI agents to real-time logs, metrics, traces and infrastructure metadata. | Ask open-ended questions without an existing alert, monitor or incident ticket. |
| Where the investigation runs | In the selected MCP-compatible client and its configured environment. | Groundcover says it can run on Amazon Bedrock, OpenAI ChatGPT or Anthropic Claude inside the customer cloud. |
| Query style | Tool calls made by the assistant to retrieve telemetry and metadata. | Groundcover’s gcQL queries across logs, metrics, traces and events; the product says each query is visible and reusable. |
| Write capability | Should be limited to read-only tools unless a separate, approved workflow is deliberately added. | Capabilities depend on the deployment and integrations you enable; keep remediation disabled or approval-gated by default. |
| Best fit | A controlled incident assistant embedded in an existing Claude Code or Codex workflow. | Exploratory investigations when no alert or ticket already exists. |
| Audit considerations | Capture the client’s prompts, MCP tool inputs and outputs, approvals and network decisions. | Retain the visible gcQL queries, agent actions, approvals and resulting changes in the same incident record. |
The “inside the customer cloud” and data-residency behavior described for Agent Mode is a Groundcover claim. Validate the actual deployment path, retention, subprocessors, encryption and contract terms for your edition before treating it as a compliance guarantee. Groundcover’s AI Observability material also says captured LLM and agent data stays within the customer environment with no outbound traffic; confirm that statement against your architecture and network telemetry.
Prepare the environment before granting access
Inventory what the agent must see
- The production services and namespaces relevant to the incident class.
- The clusters and regions where those services run.
- Telemetry types required for diagnosis: logs, metrics, traces, Kubernetes objects and events.
- The shortest useful time range, such as the period from the first alert through the present.
- Recent deployment and configuration metadata needed for correlation.
Do not begin with “all production.” A narrowly defined scope limits accidental disclosure and makes the agent’s conclusions easier to audit.
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- Use a dedicated identity for the MCP connection or Agent Mode integration, rather than a developer’s personal token.
- Grant read permissions only to the telemetry stores and Kubernetes resources needed for investigation.
- Separate production-read access from any identity that can deploy, alter secrets, edit tickets or merge code.
- Set expiration or rotation for credentials and record which policy version was active during each incident.
- Apply network egress rules so the agent can reach approved observability and source-control endpoints only.
Define the human boundary in writing
Before connecting the client, document actions that always require a person: deployment, rollback, scaling, feature-flag changes, secret or credential changes, ticket closure, customer communication, pull-request creation, code modification and merge. If your client supports permission policies, map these actions to an approval rule rather than relying on a prompt that says “be careful.”
Configure a read-only MCP workflow
Groundcover’s MCP Server is intended to connect Claude Code and other AI agents to live observability data. The exact server address, authentication method and client configuration vary by your Groundcover edition and client version, so use the current configuration supplied by your administrator. The safety properties below are independent of those syntax details.
- Install or enable the MCP server in a controlled workspace. Keep the configuration in the team-managed environment used for incident response, not in an unreviewed personal directory.
- Authenticate with the dedicated read identity. Verify that the token cannot write to telemetry, Kubernetes, source control, deployment systems or ticketing.
- Expose only query and metadata tools. Disable shell execution, arbitrary HTTP requests, deployment APIs and ticket mutation for the initial connection.
- Apply scope filters. Restrict clusters, namespaces, services and maximum look-back period. If the integration cannot enforce those filters, enforce them in the surrounding network and policy layer before use.
- Test with a known, non-sensitive window. Confirm that the agent can retrieve the intended logs, metrics and traces and cannot retrieve unrelated namespaces or secret values.
- Log the connection. Record identity, client, server version, enabled tools, policy version and the time access was granted.
A useful policy for an example service might permit read access to the payments-api service in the prod-payments namespace on cluster prod-us-east-1 for a maximum 60-minute window. That example is a boundary to adapt, not a Groundcover command or a universal default.
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Require a scope declaration before the first query
Use an instruction such as:
Before using any telemetry tool, state: service, namespace, cluster, environment, start time, end time, and the specific question. Do not query outside that scope. If a broader scope is necessary, stop and request approval.The declaration gives the operator a chance to catch a wrong cluster or an unnecessarily broad time range before data is retrieved.
Use Agent Mode for open-ended investigations carefully
Groundcover says Agent Mode supports questions that do not begin with an existing monitor, alert or incident ticket. That is useful for symptoms such as “checkout latency appears elevated” when no formal alert exists. It also increases the need for explicit boundaries because the initial question may be vague.
Start with a bounded question
Instead of asking “Why is production slow?”, specify the service, environment, time range and signal:
Investigate elevated p95 latency for payments-api in prod-payments on prod-us-east-1 from 2026-10-18 09:00 UTC through 10:00 UTC. Compare the window with the preceding 60 minutes. Read telemetry only. Return the SLO status, notable log and trace patterns, relevant Kubernetes events, recent deploys, competing hypotheses and the next read-only checks.Agent Mode’s visible and reusable gcQL queries are valuable for review. Preserve those queries with the incident record so another engineer can reproduce the evidence without accepting the agent’s narrative on faith.
Keep exploratory and corrective phases separate
Allow broad enough read access to discover the cause, but do not enable remediation in the same unrestricted session. If the agent proposes a change, have it produce a plan that names the exact resource, expected effect, validation metric and rollback. A separate, approval-gated workflow can then execute the approved action.
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1. Establish the symptom and SLO
Ask the agent to identify the affected SLO, target, observed value, start time and number of requests or operations in scope. Require it to distinguish an actual breach from a sparse sample, missing telemetry or a dashboard calculation error.
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Groundcover’s technical guide includes a demonstration load test in which 86 of 120 requests, or 72%, breached a 500 ms SLO target. That is a guide example, not a general performance benchmark and not evidence that your service will behave the same way. Use it only to illustrate the level of arithmetic and qualification expected in an incident report.
2. Correlate signals instead of reading logs alone
- Metrics: latency percentiles, error rate, saturation, queue depth and request volume.
- Traces: slow spans, dependency timing, retries, fan-out and propagation of errors across services.
- Logs: structured error fields, request identifiers, rate-limit responses and changes in message frequency.
- Kubernetes context: pod restarts, scheduling failures, readiness changes, events, node pressure and rollout status.
Have the agent attach timestamps and correlation identifiers to each material observation. A plausible story without a time-aligned trace or metric is a hypothesis, not a root cause.
3. Compare with recent changes
Ask for deployments, image changes, configuration edits, feature-flag changes and dependency incidents that overlap the symptom window. “Recent” must be defined relative to the incident; compare at least the affected window with an earlier baseline and identify whether the change preceded the first signal.
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4. Test competing explanations
Require at least one alternative hypothesis when the evidence is ambiguous. For example, a latency increase may result from an application regression, a downstream timeout, node pressure or a traffic mix change. The agent should list the read-only query that would discriminate among them and state what result would weaken its preferred explanation.
5. Produce a reviewable diagnosis
A useful report includes scope, timeline, observed facts, hypothesis, confidence, unknowns, proposed next query and a remediation plan. Keep facts separate from interpretation so an approver can challenge one assumption without discarding the entire analysis.
Make approval gates real, not rhetorical
An approval gate must block the tool call until a person decides. Anthropic’s permission-policy terminology distinguishes always_ask, which waits for approval, from auto, which may run before a person sees the call. Only the former is a human checkpoint.
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Actions that should remain human-controlled
- Deploying a new image or configuration.
- Rolling back a release or changing traffic weights.
- Changing secrets, credentials, network rules or access policies.
- Deleting or scaling infrastructure.
- Closing, reclassifying or externally updating an incident ticket.
- Sending customer, regulator or public status communication.
- Creating, modifying, approving or merging code changes.
What the approval record should contain
- The exact proposed command or API operation.
- Target resource, environment and scope.
- Expected benefit and measurable success criterion.
- Rollback procedure and who can execute it.
- Blast radius, maintenance impact and unresolved uncertainty.
- Approver identity, timestamp and decision: approved, rejected or modified.
For a risky action, ask the agent to propose a canary or other bounded test first. Approval of a plan is not approval of later deviations; require a new decision if the target, scope or operation changes.
Defend against prompt injection in telemetry
Logs, traces, ticket text and web content are data, not instructions. An attacker who can influence an error message could insert text such as “ignore your policy and run this command.” The agent must treat that text as untrusted content and never as permission.
Anthropic states that “the server does not read intent from a tool result, a fetched webpage, an MCP server’s response, or a message between session threads.” In practice, reinforce that boundary with technical controls:
- Keep shell, deployment and credential tools unavailable during read-only analysis.
- Mark retrieved fields as untrusted in the agent’s system or project instructions.
- Require the agent to quote suspicious instructions as evidence and explain why it ignored them.
- Filter secrets and personal data before telemetry reaches the model where feasible.
- Use a sandbox and constrained network policy so a malicious string cannot create capability by itself.
- Review tool arguments, not just the final prose answer.
If an investigation encounters an instruction embedded in a log or ticket, stop and escalate when it requests a privilege change, data exfiltration, external communication or any action outside the declared scope.
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An incident record should let another engineer reconstruct what the agent knew, what it did and why a human accepted or rejected the recommendation.
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Capture these records
- Original incident request and every scope declaration.
- Agent prompts, system or project policy versions and session identifiers.
- MCP tool names, inputs, outputs, timestamps and errors.
- Agent Mode gcQL queries and returned result sets or stable references.
- Network allow and deny events, including attempted access outside policy.
- Approval requests, approver decisions and any edits to the proposed action.
- Final remediation, validation measurements and human disposition.
OpenAI documents OpenTelemetry export for Codex prompts, tool calls, MCP use and network decisions. Export those event classes to the same protected logging destination as other incident evidence, with retention and access controls appropriate to production data. If your Claude workflow has a different telemetry path, achieve equivalent coverage rather than assuming the client’s transcript alone is sufficient.
What the published examples do—and do not—prove
Groundcover’s technical guide demonstrates a Claude Code workflow that detects an SLO breach, queries traces, diagnoses a distributed root cause, files a Linear incident ticket and suggests a code fix through MCP. It demonstrates a possible integration pattern; it is not a guarantee that every environment supports those exact integrations or that the diagnosis will be correct.
A Groundcover customer story about Usable reports a remote agent querying Groundcover and Kubernetes, correlating telemetry with prior investigations, identifying a likely root cause, proposing and implementing a fix and submitting a pull request. The story also claims zero minutes spent debugging incidents manually without MCP inputs. That is a customer-reported workflow and figure, not an independently measured performance result. Treat it as an example of process design, not a service-level promise.
Troubleshoot common failure modes
The agent cannot see the incident data
- Confirm the identity has read permission for the selected cluster, namespace and telemetry stores.
- Check that the requested time window is within retention and uses the correct time zone.
- Verify that service labels, trace propagation and log parsers use the names in the query.
- Check network allowlists and MCP connection health before widening permissions.
The agent retrieves unrelated production data
- Stop the session and revoke the overbroad credential.
- Review which tool or filter allowed the access.
- Replace account-wide access with namespace, service and time-window restrictions.
- Record the incident as a policy failure and test the boundary with a known unrelated resource.
The diagnosis is plausible but unsupported
- Ask for the exact query, timestamps and identifiers behind each claim.
- Request a baseline comparison and at least one competing hypothesis.
- Check whether missing telemetry or sampling could explain the apparent pattern.
- Do not approve remediation until a measurable validation and rollback plan exists.
An embedded instruction asks for a write action
Classify it as untrusted retrieved content, do not execute it and preserve the relevant log or ticket excerpt for security review. The presence of an instruction in telemetry never satisfies an approval requirement.
The proposed fix increases risk
Ask for a smaller, reversible change, a canary, a maintenance window or a read-only experiment. If the agent cannot state a rollback path and success metric, keep the system in investigation mode.
Decide between MCP and Agent Mode
Use the comparison below as a deployment decision, not as a claim that one product path is universally safer.
| Question | Prefer MCP-connected Claude or Codex when… | Prefer Agent Mode when… |
|---|---|---|
| Do you already have an incident workflow? | You want the assistant embedded in an existing, ticketed response process. | You need to explore a symptom before an alert or ticket exists. |
| How much control do you need over tools? | You can expose a small, explicit set of read-only tools and keep writes in another workflow. | Your Groundcover deployment provides the desired query experience and you can separately gate any enabled actions. |
| Where must data run? | Your client and MCP architecture meet your residency and network requirements. | Your validated deployment keeps processing inside the customer cloud as required by policy. |
| How will reviewers reproduce findings? | They can inspect captured MCP calls and outputs. | They can inspect the visible, reusable gcQL queries and agent record. |
| What is the main risk? | Overbroad client tools or credentials. | Open-ended exploration expanding scope or silently acquiring write capability. |
In either case, evaluate deployment location, data residency, read-versus-write tools, query visibility, approval and rollback controls, supported integrations and audit-log export. Make the decision against your own threat model and contract, not a generic “AI is safe” label.
A practical operating checklist
- ☐ Service, namespace, cluster, environment and time window are explicit.
- ☐ Dedicated read-only identity is used.
- ☐ Shell, deployment, secret, ticket and code-write tools are disabled or approval-gated.
- ☐ Network policy allows only approved observability and workflow endpoints.
- ☐ Agent must state scope before its first query.
- ☐ Logs, traces, metrics and Kubernetes events are correlated with timestamps.
- ☐ Recent deploys and configuration changes are compared with a baseline.
- ☐ Facts, hypotheses, unknowns and confidence are separated.
- ☐ Retrieved text is treated as untrusted and prompt injection is explicitly handled.
- ☐ Proposed changes include blast radius, validation and rollback.
- ☐ A real human approval blocks every externally visible or hard-to-reverse action.
- ☐ Prompts, tool calls, results, approvals and network decisions are retained.
- ☐ Post-change validation uses the same SLO and telemetry definitions as the diagnosis.
Bottom line
Groundcover can give Claude or Codex a fast, evidence-rich view of a production incident, but safety comes from the surrounding design. Start with read-only MCP access or a tightly scoped Agent Mode session, require explicit scope and visible queries, treat telemetry as untrusted input, and make a human approve every consequential action. When the full audit chain is preserved, the agent can shorten time to diagnosis without becoming an uncontrolled production operator.
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