get_pipelinerun_logs

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

With no annotations provided, the description carries the full burden and does so effectively by disclosing key behavioral traits: it fetches logs from all pods, prioritizes failed pods, manages token budgets automatically under certain conditions, and returns a dictionary with metadata. It also mentions adaptive volume management, which adds context beyond basic functionality. However, it lacks details on error handling or rate limits, which could be important for a tool interacting with Kubernetes.

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

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

The description is appropriately sized and front-loaded: the first sentence states the core purpose, followed by key behavioral details, then a structured list of args and returns. Every sentence earns its place by adding value, with no redundant or vague language, making it efficient and easy to parse.

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

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

Given the complexity (9 parameters, no annotations, but with an output schema), the description is complete enough: it explains the tool's purpose, behavior, all parameters, and return structure. The output schema exists, so the description need not detail return values beyond the high-level summary provided. It covers essential context for a log-fetching tool in a Kubernetes environment.

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

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

The schema description coverage is 0%, so the description must compensate, which it does by listing all 9 parameters with brief explanations (e.g., 'Clean and format logs', 'Lines from end', 'Maximum tokens for output'). This adds significant meaning beyond the schema's titles and types, clarifying defaults and optional usage. However, it does not provide examples or deeper semantics for complex parameters like 'since_time' format, leaving some gaps.

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

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

The description clearly states the specific action ('Fetch logs from all pods in a Tekton PipelineRun') and resource ('Tekton PipelineRun'), distinguishing it from siblings like 'analyze_pod_logs_hybrid' or 'smart_summarize_pod_logs' by specifying it's for PipelineRun logs with adaptive volume management. The verb 'fetch' is precise and the scope is well-defined.

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

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

The description provides clear context on when to use this tool ('with adaptive volume management') and mentions prioritization of failed pods and token budget management, which helps differentiate it from simpler log-fetching tools. However, it does not explicitly state when NOT to use it or name specific alternatives among the siblings, such as 'analyze_logs' or 'get_tekton_pipeline_runs_status', which could provide more precise guidance.

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