teach

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

Annotations already declare that this is a non-read-only, non-destructive operation, so the agent knows it performs a write. The description adds useful behavioral context by saying it is LLM-fired and silent, implying background invocation and minimal user-facing output. It does not explain persistence, validation behavior, or side effects beyond what the annotations provide.

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 a single tightly written sentence with the core action front-loaded. There is no wasted text, and the parenthetical conveys invocation context efficiently.

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 32 parameters, multiple sibling teach/recall tools, and no output schema, the description is extremely thin. It does not explain what gets stored, how validation works, or how it relates to teach_lookup and teach_playbook, leaving substantial contextual gaps despite the rich 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 description coverage is 100%, so the schema already documents all 32 parameters in detail. The description adds no parameter-level meaning beyond what the schema provides, making the baseline 3 appropriate.

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 specific action and object: recording a query-to-resource mapping for later recall. The parenthetical '(LLM-fired, silent)' further identifies the intended invocation mode. It does not explicitly distinguish itself from sibling tools such as teach_lookup, teach_pattern, or teach_playbook, which keeps it from a 5.

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 implies usage through 'for future recall' and '(LLM-fired, silent)', but it gives no explicit guidance on when to use this tool versus teach_lookup, teach_pattern, teach_playbook, or learnings_confirm. An agent must infer the intended situation from the name and sibling names alone.

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