reflect

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

Annotations declare readOnlyHint=false and destructiveHint=false, but the description adds no additional behavioral context. It does not explain what 'candidate' means, whether events are consumed or preserved, what side effects occur, or if the operation is reversible. The description fails to add value beyond the annotations.

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 a single concise sentence that front-loads the main action. It is efficient with no wasted words, but could benefit from additional structure or bullet points for clarity.

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 has 3 parameters (1 required), an output schema, and is a write operation, the description is incomplete. It does not describe the return value, clarify the nature of 'candidate lesson', or provide enough context for the agent to use the tool confidently. The presence of an output schema reduces the burden slightly but not enough.

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 input schema has 0% description coverage for parameters, yet the description does not explain the purpose or behavior of any parameter (event_ids, suggestion, lesson_text). The agent receives no guidance on how these parameters influence the lesson creation, which is critical for correct invocation.

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 'Create a candidate lesson from evidence events' clearly uses a verb ('Create') and resource ('candidate lesson') and specifies the source ('evidence events'). However, it does not differentiate from the sibling tool 'learn.reflect' or other learning tools like 'learn.feedback', which limits its distinctiveness.

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?

No guidance is provided on when to use this tool versus alternatives like 'learn.evaluate' or 'learn.improvements'. There are no explicit when-to-use or when-not-to-use conditions, leaving the agent without contextual decision support.

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