mark_lesson_followed

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

With no annotations, the description explains the tool's role in the self-improvement loop and clarifies the semantic difference between followed and applicable. It omits details on idempotency or side effects, but for a simple feedback tool the described behavior is sufficient.

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, front-loaded with purpose, includes signature and crucial usage tip, no redundant words.

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 simple parameter set, full schema coverage, and presence of an output schema, the description covers the when, why, and how-to-use nuances. The only missing aspect is return value description, which is handled by the output schema.

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 already covers all 4 parameters at 100%, but the description adds a concrete function signature and, more importantly, resolves the ambiguity between followed and applicable through explicit usage rules, which is beyond the schema.

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 clear verb-resource pairing: 'Report whether a surfaced lesson changed your behaviour.' It distinguishes from siblings like find_lessons (retrieval) by focusing on post-hoc feedback, and the signature example reinforces the tool's role.

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?

Explicitly instructs 'Call after acting on a lesson' and provides a key disambiguation: 'Use applicable=False when the lesson was irrelevant, not followed=False.' This gives direct when-to-use guidance relative to alternatives.

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