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

The description explains the effect of approval (incorporating insight), which adds behavioral context beyond annotations. Annotations indicate idempotent and non-destructive; description is consistent and provides meaningful insight about state changes.

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?

A single, front-loaded sentence that conveys the purpose and effect without any wasted words. It is 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?

For a simple tool with one parameter, good annotations, and no output schema, the description is sufficient. It could optionally mention that the reflection must exist or that approval is permanent, but it is not necessary.

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%, and the parameter 'id' is already well-described in the schema. The description adds no extra semantics beyond what the schema provides, leading to a baseline score of 3.

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 action (approve), the object (bot-generated reflection), and the outcome (incorporate insight into future behavior). It distinguishes from sibling tools like 'reject_reflection' and 'list_reflections'.

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 tells the main use case but does not explicitly mention when not to use it or suggest alternatives (e.g., 'neuron_reject_reflection' for rejecting). A clearer 'when to use vs. alternatives' statement would improve it.

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