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

Annotations already declare readOnly, idempotent, non-destructive, and closed-world, so 'Read-only' and 'Takes no input' add little. The genuinely additive content is the return-shape disclosure (ids and display names, plus a has_published_multiples flag on sub-verticals), which matters because no output schema exists.

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 short sentences, front-loaded with the operation and immediately followed by the return contents and the routing advice. Nothing is padded and no sentence duplicates another.

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?

With no output schema, the description must convey what comes back, and it does (ids, display names, has_published_multiples). Combined with the zero-parameter schema and read-only annotations, an agent has everything needed to call and use it 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?

The tool takes zero parameters, which is the baseline-4 case; the description confirms 'Takes no input' consistently with the empty schema. There is no parameter semantics to elaborate, so no further credit or penalty 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 uses a specific verb (lists) plus a precise resource (every industry and sub-vertical ExitValue.ai tracks) and even names the return fields (ids, display names, has_published_multiples). This clearly distinguishes it from compare_industries, get_multiples_trend, and get_valuation_multiples, which all operate on industries rather than enumerate them.

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?

It gives an explicit trigger ('Use it first when you are unsure of an id') and explains how the output feeds siblings ('the other tools accept these ids or plain-English names'), which also tells the agent when calling this tool is optional. That is a clear when-to-use plus alternative-behavior statement.

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