leadquasar

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

With no annotations, the description must carry behavioral weight. It does so by saying counts are real rather than estimates, data is from 7.5 million live records, and businesses are verified US B2B. It stops short of describing the response format or any limitations, but the operation is a low-risk read-only count.

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 sentences with the action verb front-loaded and no wasted words. The examples are compact and illustrative, and the scope facts ('7.5 million live records') justify the tool without bloating the description.

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 simple count tool with only optional parameters, the description covers purpose, filters, and the main output dimensions (total count, email count, phone count). Since there is no output schema, it could be more explicit about the exact response shape, but an agent has enough to invoke 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 schema already documents all three parameters at 100% coverage, setting the baseline at 3. The description adds useful semantic context by combining filters ('industry, state and/or city') and by showing the email/phone count dimension implied by examples, which helps an agent understand how the parameters compose.

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 opens with a specific verb and resource: 'Count verified US B2B businesses... matching an industry, state and/or city,' and closes with 'Returns real counts,' which separates it from the record-returning search_businesses sibling. It does not name sibling tools explicitly, so it falls just short of full sibling differentiation.

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?

'Use this to answer questions like...' provides concrete usage examples that map natural-language questions to parameters. It does not state when NOT to use it or direct the agent to search_businesses for record-level queries, so exclusions are missing.

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

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.

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

With no annotations provided, the description must carry behavioral disclosure on its own, and it does well: it explicitly states that name, company, email, phone, and address are never returned. This is a major behavioral constraint not visible in the input schema. It stops short of describing sampling/ordering behavior or service limits, but the key risk is clearly disclosed.

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 efficient sentences with the main purpose front-loaded and the critical contact-detail exclusion placed immediately after. There is no filler, no repetition of schema content, and every sentence 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?

Given no output schema and no annotations, the description covers the essential invocation context: it returns a sample, indicates the kinds of fields included, and clearly states what it will never return. It could add an explicit note about non-exhaustive sampling or point to count_businesses for totals, but an agent has enough to call and interpret 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?

The input schema has 100% description coverage for all four parameters, so the baseline of 3 applies. The description mentions industry and location as output content but adds no extra semantics to city, state, limit, or industry beyond what the schema already provides.

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 opens with a specific verb and object: 'Return a sample of matching businesses,' and names the key output dimensions (industry, location, data-quality signals). It also draws a clear line from sibling count/list tools by emphasizing that this returns a sample, not a count or an industry 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?

It gives clear context: use this tool to see what businesses exist and how reachable they are, not to obtain contact information. The 'not who to call' exclusion is explicit, but it does not name sibling alternatives such as count_businesses or list_industries, so it falls just short of full alternative routing.

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