analyze_failed_pipeline

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

No annotations are provided, so the description carries full burden. It mentions what the tool does (analysis, fetching details, providing recommendations) but doesn't disclose behavioral traits like whether it requires specific permissions, has rate limits, modifies resources, or handles edge cases. For a diagnostic tool with no annotation coverage, this leaves significant behavioral gaps.

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 well-structured and front-loaded with the core purpose, followed by clear parameter and return value sections. Every sentence earns its place: the first states the overall purpose, the second elaborates on key functions, and the structured sections provide essential details without redundancy.

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 tool's complexity (root cause analysis), lack of annotations, and presence of an output schema (which documents return values), the description is reasonably complete. It explains the purpose, parameters, and return structure. However, for a diagnostic tool with no behavioral annotations, it could benefit from more operational context like prerequisites or limitations.

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?

Schema description coverage is 0%, but the description compensates by clearly explaining both parameters in the Args section: 'namespace: Kubernetes namespace of the PipelineRun' and 'pipeline_run: Name of the failed PipelineRun'. This adds meaningful semantic context beyond the bare schema, though it doesn't provide format examples or constraints.

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 ('perform root cause analysis'), target resource ('failed Tekton PipelineRun'), and scope ('fetches pipeline/task details, analyzes logs for errors, provides remediation recommendations'). It distinguishes from siblings like 'analyze_logs' or 'get_pipelinerun_logs' by focusing specifically on root cause analysis of failed pipelines rather than general log analysis or status retrieval.

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 implies usage context ('failed Tekton PipelineRun') but doesn't explicitly state when to use this tool versus alternatives like 'analyze_logs', 'get_pipelinerun_logs', or 'find_pipeline'. It provides basic context but lacks explicit guidance on tool selection criteria or exclusions.

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