pm_workflow_post_launch_learning

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

No annotations are provided, so the description carries the full burden. It discloses that the tool returns a Markdown workflow plan and describes the effort level as 'comprehensive.' It also lists the steps and what the plan includes. There is no mention of destructive actions or auth needs, which is acceptable for a workflow planning tool.

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 with a bolded title, bulleted steps, and clear sections for arguments and returns. It is front-loaded with the purpose. While it could be slightly more concise (e.g., the 'Steps' list could be inline), it remains easy to scan.

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?

The tool has 2 parameters (one required), no nested objects, and no output schema. The description explains the return value (Markdown workflow plan) and provides execution guidance (AI orchestrates steps). This is sufficient for a workflow generation tool.

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 coverage is 100% (both parameters have descriptions). The description adds context beyond the schema by explaining 'topic' as 'The subject or feature for this workflow' and 'context' as 'Additional context, constraints, or requirements.' This reinforces the schema but does not introduce new syntax details.

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 it is a 'Post-Launch Learning workflow' for setting up measurement, evaluating results, and capturing learnings after a feature ships. It lists specific steps (pm_instrumentation_spec, pm_dashboard_requirements, etc.), distinguishing it from sibling workflows like pm_workflow_feature_kickoff or pm_workflow_lean_startup.

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 says 'Use this tool to get a complete workflow plan' and implies post-launch context. It notes that the AI orchestrates execution by calling each step's tool. However, it does not explicitly state when not to use this tool or mention alternative workflows for different phases.

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