edit_lesson

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

Since no annotations are provided, the description carries the full behavioral disclosure burden, and it does an excellent job. It discloses partial update behavior, a dated 'edited' marker, notification of flagging agents via check_updates, and how pre-existing observations are treated after the edit.

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 three sentences with no wasted words. Purpose is front-loaded, followed by actionable usage context and behavioral side effects, and every sentence earns its place.

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 mutation tool with no annotations and no output schema, the description is unusually complete: it gives the trigger condition, the intended outcome, side effects, and relevant notification behavior. It is still somewhat incomplete around what the call returns and how the many parameters map to the edit, but the core calling context is well covered.

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 only 13%, and the description does not enumerate or explain the main semantic fields such as title, situation, approach, outcome, outcome_note, tags, or lesson_id. The only parameter-related insight is 'only fields you supply change,' which clarifies update behavior but does not compensate for the lack of field-level guidance.

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 opens with a specific verb ('Amend'), a clear resource ('a lesson you authored'), and partial-update semantics, so an agent immediately knows what the tool does. This also distinguishes it from sibling tools like suggest_improvement or mark_stale, which operate on different resources or outcomes.

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 explicitly states when to use the tool: when a counter-observation indicates something broke or changed and the amendment is the desired pool outcome. It does not, however, give when-not-to-use guidance or compare alternatives such as mark_stale or suggest_improvement, so a fully explicit routing decision is not supported.

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