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Get Logs

get_logs
Read-onlyIdempotent

Fetch Kubernetes logs from pods, deployments, jobs, or resources matching a label selector, and adjust tail, container, namespace, or timestamps as needed.

Instructions

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

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameNo
tailNo
containerNo
namespaceNo
timestampsNo
resource_typeYes
since_secondsNo
label_selectorNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

B3/5.0
Behavior3/5

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, covering the safety profile. The description adds that logs can come from multiple resource types or label-selected resources, but it does not explain behavior such as whether a name or label_selector is required, how tail/default limits work, or what output to expect. This adds modest context beyond annotations but leaves several behavioral questions unanswered.

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

Conciseness5/5

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

One compact sentence with no filler words, and the key action is front-loaded. It conveys the core scope efficiently while leaving necessary details to other dimensions.

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?

For a tool with 8 parameters, one required parameter, no output schema, and no parameter descriptions, a single sentence is incomplete. It does not specify valid resource_type values, the relationship between name and label_selector, default namespace behavior, or the response format. Annotations cover read-only/idempotent safety but not the operational details needed for correct invocation.

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

Parameters2/5

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

Schema description coverage is 0%, so the description carries the burden of explaining parameters. It only clarifies label_selector and implies resource_type values (pods, deployments, jobs); it does not explain name, tail, container, namespace, timestamps, or since_seconds. Parameter names are self-descriptive, but the description fails to compensate for the absent schema documentation.

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?

The description uses a specific verb ('Get') and a clear resource scope: logs from pods, deployments, jobs, or resources matched by label selector. It is not a tautology, and the mention of multiple resource types distinguishes it somewhat from the sibling get_pod_logs, though it does not explicitly name that alternative.

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?

There is no guidance about when to choose get_logs over get_pod_logs or other list/get siblings. The description only states what the tool does, not when to use it or when not to use it, so an agent must infer the intended context.

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