answer

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

With no annotations provided, the description carries the full burden. It discloses that the answer is LLM-generated, grounded only in stored memories (RAG), and returns answer text plus supporting sources. This is strong context, though it could also mention behavior when no memories are found or the effects of kiosk_mode; however, those are partially captured in the schema.

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?

Two clearly structured sentences: first states purpose and grounding, second gives selection guidance and return value. Every sentence adds value, with no filler or repetition of schema details.

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 complexity (5 parameters, output schema), the description covers purpose, usage guidance, grounding behavior, and return contents. It doesn't explicitly discuss failure modes such as no memory found, but that is addressed by the kiosk_mode parameter and output schema, making the description adequately complete for an 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?

Schema coverage is 100%: every parameter has a clear description. The tool description adds no parameter-specific meaning beyond what the schema provides, so the baseline score of 3 is 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 clearly states the tool's function: 'Ask a natural-language question and get an LLM-generated answer grounded ONLY in the agent's stored memories (RAG).' It also explicitly contrasts with a sibling tool ('Prefer this over `recall`'), distinguishing a synthesized answer from a ranked list.

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 direct usage guidance: 'Prefer this over `recall` when you need a synthesized answer rather than a ranked list.' This tells the agent exactly when to choose this tool over a close alternative, and the rest of the description implies when not to use it (e.g., when a ranked list is desired).

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