learn_pattern

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

With no annotations provided, the description carries full burden. It discloses model trust gates (only Opus/Fable write directly, others stage) and that patterns are available in future sessions. However, it does not mention any destructive effects or authentication requirements.

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, front-loaded with purpose, then usage guidelines, then trust gates. No wasted words; each sentence adds essential 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?

Given the tool saves to a knowledge base and an output schema exists (though not shown in input), the description covers purpose, usage, and trust model. It could mention what happens after staging or if patterns can be overwritten, but overall sufficient for an AI agent.

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 provides clear descriptions for each parameter (api, code, description), so the tool description does not need to add parameter details. The description adds contextual usage info but no new parameter semantics.

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 saves a validated working pattern to the RAG knowledge base. It uses specific verbs ('Save', 'call') and identifies the resource, distinguishing it from sibling tools like search_maya_docs or Maya operation tools.

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?

Provides explicit when to use: after successful operation when search_maya_docs returned low relevance (< 60%). Also specifies alternatives (search_maya_docs) and trust gates for different model tiers, giving clear context for invocation.

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