record_learning

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

With no annotations provided, the description carries the burden. It discloses key behavioral details such as automatic correction of repository names to owning projects, discarding unregistered names, and the presence of project/project_hint in the response, but it does not explicitly state that the operation creates a persistent record or mention any side effects beyond writing.

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 well-organized into purpose and usage/field guidance sections, uses concise bullet-like phrasing, and includes an example title, making it efficient and easy to follow without unnecessary verbosity.

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 that an output schema exists, the description doesn't need to detail return values, but it does mention response hints (project/project_hint) and references list_sources for project validation, providing sufficient context for an agent to invoke the tool correctly and interpret results.

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 schema has zero descriptions, so the description must compensate. It covers important parameters (title, prevention, code_refs, project) with concrete examples and rules, while less complex parameters (author, solution, tags, etc.) are self-explanatory from their names, providing adequate overall semantic coverage.

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 that the tool records lessons learned from struggles, bugs, and mistakes to prevent repeating the same issues, which is a specific and distinct purpose from sibling tools like record_note or record_decision.

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?

It provides explicit when-to-use criteria (e.g., bugs that took over 30 minutes, behavior differing from docs, pitfalls) and detailed field-level guidance including title format, prevention rules, code_refs semantics, and project name validation against list_sources.

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