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ACRT-EVAL
by ACRT-EVAL

get_logs

Retrieve logs from Kubernetes pods, deployments, jobs, or labeled resources by name or selector to debug workloads and inspect container output.

Instructions

Get logs from pods, deployments, jobs, or resources matching a label selector

Input Schema

NameRequiredDescriptionDefault
nameNoName of the resource
tailNoNumber of lines to show from end of logs
sinceNoGet logs since relative time in seconds
containerNoContainer name (required when pod has multiple containers)
namespaceNoNamespace of the resourcedefault
timestampsNoInclude timestamps in logs
resourceTypeYesType of resource to get logs from
labelSelectorNoLabel selector to filter resources

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.4

TDQS

C2.9/5.0
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full disclosure burden, yet it says nothing about permissions needed, whether logs from multiple matching pods are merged, how errors on missing containers behave, or any output size limits. Only the read-only nature is implied.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

A single front-loaded sentence with no filler. It is efficient, though its brevity leaves no room for the behavioral and usage context an 8-parameter tool needs.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With 8 parameters, no annotations, and no output schema, the description should explain multi-container behavior, label-selector fan-out across pods, and default log volume. None of that is present, leaving significant gaps for a tool this parameter-rich.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so parameters like tail, since, container, and labelSelector are already documented in the schema. The description adds only the label-selector framing, with no extra detail on default tail limits, since vs timestamps interaction, or multi-pod aggregation behavior.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb (get) and resource (logs) and enumerates the supported target types (pods, deployments, jobs), which clearly distinguishes it from siblings like list_pods or describe_pod. Minor imprecision: it says 'or resources matching a label selector' while the schema's resourceType enum only allows pod/deployment/job.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance on when to prefer this tool over describe_pod or list_* siblings, and no mention of prerequisites such as the container requirement for multi-container pods. The agent must infer all selection criteria from the purpose sentence alone.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.