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

With no annotations, the description itself carries the behavioral disclosure. It states that the tool returns industries and their verified business counts, which is enough for a simple read-only listing, but it omits any details about ordering, potential size of the response, or whether the list is exhaustive.

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 two short sentences with no filler. The first states exactly what the tool does, and the second gives practical usage guidance. Every word earns its place.

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?

For a no-parameter, simple listing tool, the description covers the return value (industries plus counts) and the use case. It does not fully explain output ordering or pagination, but those are unlikely to matter for a small enum-like industry list.

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 has zero parameters, so the baseline is 4. The description still contributes useful context by revealing that each industry includes a count of verified businesses, which clarifies the returned data though no input semantics are needed.

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?

Uses a specific verb ('list') plus a resource ('industries LeadQuasar covers') and describes the key output (counts of verified businesses). It clearly differentiates itself from sibling tools like count_businesses and search_businesses by focusing on the industry vocabulary coverage.

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 guidance: 'Use this first when unsure what industry wording will match.' This tells the agent the trigger condition for calling the tool, though it does not explicitly name alternative tools or list when not to use it.

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