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

Annotations already cover safety traits: readOnlyHint=true, idempotentHint=true, destructiveHint=false. The description adds valuable behavioral context beyond annotations: it promises exactly 10 questions, lists data sources, defines difficulty tiers (standard/tough/hostile) and risk levels, and states a cost range of ~$0.01-0.03/call.

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 front-loaded with the core purpose and then supplies supporting detail. The category list is somewhat long but useful for understanding output scope; overall it is dense without being rambling.

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?

An output schema exists, so return values need not be explained. Given the read-only annotations and the rich schema, the description covers scope, data sources, output volume, difficulty/risk ratings, and cost, leaving no critical gaps for invocation.

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%, and the only parameter (ticker) is fully documented in the schema with examples. The description adds no parameter-level syntax, format, or constraints beyond what the schema already provides, so the baseline of 3 applies.

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 verb and resource: 'Generate 10 tough investor questions for an NZX company with data-backed suggested responses.' It clearly distinguishes the output from generic Q&A or research tools by specifying simulated investor questions, but it does not explicitly name sibling alternatives like get_deep_research_qa or simulate_event.

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?

Usage context is implied by 'investor calls or roadshows,' which suggests when an IR team would use it. There is no explicit when-to-use versus alternatives, no exclusions, and no routing to sibling tools.

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