self_improvement_extract_skill

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

The description implies a write operation (creating and promoting) but does not disclose side effects, such as whether files are created, how the skill scaffold is stored, or if it affects other data. With no annotations, the description bears the full burden and falls short.

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, front-loaded sentence that conveys the core action with no redundant words. It is appropriately brief.

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 lack of output schema and annotations, the description should explain what 'skill scaffold' means, what the return value is, and any implications. The current description leaves the agent guessing about the tool's full behavior.

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 67%, and the schema already provides decent descriptions for learningId and skillName. The description adds no additional meaning for parameters, so it meets the baseline but does not exceed.

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 ('Create') and the specific resource ('new skill scaffold from a learning entry') and includes an additional effect ('mark it as promoted'). It distinguishes from sibling tools that handle memory operations or general logging, as this tool focuses on extracting skills.

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 vs. alternatives like self_improvement_log or memory_extract. There is no mention of prerequisites (e.g., the learning entry must exist) or when not to use it.

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