learning_promote

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

Annotations only indicate the tool is not read-only and not destructive. The description adds meaningful behavioral detail: memory lessons are written to the lessons drawer, while Skill/Capability/GPT targets become proposals and never silently modify Pilot. This goes beyond the annotation flags to clarify how state may change.

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?

Two sentences with no wasted words: the main action is front-loaded, followed by the key side-effect caveat. It is appropriately sized for a tool with one parameter.

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 description covers purpose and side effects well for a simple, single-parameter tool with no output schema. It is slightly incomplete because 'evidence-backed' is left undefined and it does not specify how promotion is realized beyond the side-effect statement.

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 0%, so the description must compensate for documenting candidate_id. It only implies candidate_id is the identifier of the evidence-backed candidate, without stating where the ID comes from or its format. For a single, self-named parameter this is a minor but real gap.

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 states a specific action ('Promote') on a clear resource ('one evidence-backed learning candidate') and explains the outcome for different target types. This distinguishes it from siblings like learning_rollback and learning_observe, which clearly serve different purposes.

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 phrase 'evidence-backed learning candidate' implies a prerequisite for use, but the description does not explicitly say when to use this tool versus alternatives. It does not name learning_observe or learning_history as sources of candidates, nor learning_rollback as the reversal tool.

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