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

With no annotations, the description carries the full burden. It discloses that the output is AI-generated, rapid, and not human-validated, which is critical behavioral context. It does not mention return format or data handling, but for a low-stakes AI answer tool, the disclosed traits are substantive.

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 sentences, front-loaded with the primary use case, and every clause serves a purpose. The distinction between safe and unsafe usage is made in the second sentence without waste.

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?

Despite no output schema and no annotations, the description covers what the tool does, when to use it, when not to use it, and which alternatives to choose. This is sufficient for an agent to select and invoke the tool correctly.

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 does not add parameter-specific meaning, but the schema already documents all three parameters clearly (question, sessionId, chatHistory). No need for description to compensate.

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: to provide rapid AI-generated answers, drafts, or alternative perspectives on low-risk topics. It distinguishes itself from sibling tools by explicitly naming alternatives (askExpert, askPearlExpert) for higher-risk questions.

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 provides explicit when-to-use conditions (low-risk, exploratory, no human validation) and when-not-to-use conditions (medical, legal, financial, safety-critical), along with named alternative tools. This is exactly what the dimension expects.

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