auto_learn_session

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

No annotations are provided, so the description carries full burden. It discloses that the brain classifies and stores lessons automatically and returns a summary. However, it does not detail failure modes, auth requirements, or rate limits, which would enhance transparency.

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?

Three sentences efficiently convey purpose, usage guidance, and return value. No redundant words; each sentence adds value.

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 tool with 2 params, no output schema, and no annotations, the description covers main purpose and usage. However, it lacks details on error handling and the format of the returned summary. Overall, it is fairly complete.

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 100%, so the baseline is 3. The description adds overall context for the observations parameter but does not add significant new meaning beyond the schema's descriptions of instance_id and observations structure.

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 purpose: auto-learn from session observations without explicit learn_from_attempts calls. It distinguishes from the sibling tool learn_from_attempts by specifying 'WITHOUT explicit learn_from_attempts calls'.

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 recommends using this tool at session_end to capture all actions, including those missed by learn_from_attempts. It provides clear context but does not explicitly list alternatives, though the differentiation is implied.

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