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

With no annotations, the description must disclose all behavioral traits. It mentions updating learning memory but omits side effects such as whether it is destructive, requires special permissions, or alters existing data. The word 'attaches' implies a write operation, but no further detail is given.

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 two sentences long with no wasted words, but the first sentence 'The learning tool.' is somewhat generic and could be more informative. Overall, it is efficiently structured.

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?

For a tool with 4 parameters (including nested objects and an enum), no output schema, and no annotations, the description is severely incomplete. It does not explain the purpose of parameters, expected behavior, or return value, leaving the agent without sufficient context to use the tool correctly.

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% and the description does not explain any of the 4 parameters (decisionEventId, feedbackType, notes, metrics). It mentions 'outcome' vaguely but fails to link to the enum values or nested object structure.

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 tool attaches an outcome to a past decision and updates learning memory, which is specific and distinct from sibling tools like write_decision_event (which likely records the decision itself). However, it does not explicitly differentiate from other feedback-related tools.

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 get_learning_insights or check_* actions. The description simply states what it does without context for appropriate invocation.

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