auto_learn_session

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

With no annotations provided, the description carries full weight. It discloses that the tool 'classifies and stores lessons automatically' and returns a summary. While it does not cover auth or rate limits, it gives sufficient behavioral insight for a storage operation.

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 the purpose front-loaded. Every sentence adds value, and there is no redundancy.

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 tool's simplicity (2 params, no output schema, no annotations), the description is largely complete. It explains purpose, usage, and return. Minor gaps exist around error handling or edge cases, but overall adequate.

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 100%, so the baseline is 3. The description adds context by describing the observations as 'commands run, errors seen, solutions found', but this does not significantly enhance the schema definitions.

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 tool's action ('auto-learn'), the resource ('session observations'), and distinguishes it from the sibling tool 'learn_from_attempts' by noting it works 'WITHOUT explicit learn_from_attempts calls'. It also specifies usage at session_end.

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 ('Use at session_end') and contrasts with an alternative ('even if you forgot to call learn_from_attempts'), effectively differentiating it from the sibling tool learn_from_attempts.

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