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get_workload_logs

Read-only

Aggregate logs from all pods of a Deployment, StatefulSet, or DaemonSet, filter diagnostics like errors, warnings, panics, and stack traces, and deduplicate. Use grep for additional server-side filtering.

Instructions

Get aggregated logs from all pods of a workload (Deployment, StatefulSet, or DaemonSet). Logs are collected from all matching pods concurrently, then server-side filtered to errors, warnings, panics, and stack traces using deterministic regex patterns and deduplicated. Set grep for additional server-side filtering before that summary stage, like kubectl logs | grep PATTERN. More useful than get_pod_logs when you need logs across all replicas of a workload. If the target is a config value, feature flag, CRD field, env ref, or YAML/spec content, use search rather than logs.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
grepNooptional regular expression to keep matching log lines before diagnostic filtering, like kubectl logs | grep PATTERN
kindNoworkload kind: deployment, statefulset, or daemonset. Defaults to deployment when omitted.
nameYesworkload name
sinceNoonly return logs newer than this duration (e.g. 30s, 10m, 1h), like kubectl logs --since
previousNoreturn logs from the previous terminated container instance (e.g. for CrashLoopBackOff diagnosis), like kubectl logs -p
containerNospecific container name, defaults to all containers
namespaceYesworkload namespace
tail_linesNolines per pod (default 100)
Behavior5/5

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

Annotations indicate read-only, but description provides valuable details: concurrent collection, server-side filtering to error/warning/panic/stack traces, deterministic regex, deduplication, and grep stage. This goes well beyond the annotation.

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?

Description is concise yet complete: intentional use case, behavioral details, comparison to sibling, and exclusion guidance. Every sentence adds informational value.

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

Completeness4/5

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

No output schema exists, but the description explains the filtering and aggregation behavior sufficiently for an agent to anticipate results. Minor omission: no mention of line limits or pagination, but these are in schema and less critical for the tool's core purpose.

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 already covers all 8 parameters with descriptions (100% coverage). The description adds context for grep behavior and defaults for kind, but most parameter meaning remains schema-driven. Baseline 3 applies.

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

Purpose5/5

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

Description clearly states it gets aggregated logs from all pods of a workload, naming supported kinds (Deployment, StatefulSet, DaemonSet). Distinguishes from get_pod_logs, making the purpose unambiguous.

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

Usage Guidelines5/5

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

Explicitly says when to use this over get_pod_logs ('when you need logs across all replicas'), and directs config/spec searches to the 'search' tool. Provides a concrete kubectl analogy for grep.

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

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