learn_pattern

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

Discloses that patterns are saved for future sessions and mentions model trust gates (only Opus/Fable can write directly). No annotations exist, so the description fully handles disclosure. It does not detail what happens on duplicate patterns or error conditions.

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?

Five sentences, each serving a distinct purpose: (1) core action, (2) triggering condition, (3) benefit, (4-5) model trust gates. No redundant information.

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?

Covers key context: when to use, what it does, model restrictions, and data persistence. An output schema exists (not shown), so return values need not be described. Lacks details on idempotency or error handling but sufficient for typical usage.

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?

The input schema already includes descriptions for each property (api, code, description), covering semantics. The description does not add further information about parameters, so it meets the baseline for schema-covered parameters.

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 action ('Save a validated working pattern') and the target ('RAG knowledge base'). It distinguishes from siblings by specifying that this tool is used after search_sg_docs returns low relevance.

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 defines when to use: 'after a successful operation when search_sg_docs returned low relevance (< 60%)'. It also provides model-specific guidance about write permissions. However, it does not explicitly state when NOT to use or mention alternatives beyond the implicit condition.

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