leadquasar

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

No annotations are provided, so the description carries the full burden. It discloses that it returns real counts from 7.5 million live records, which is useful. However, it does not mention any potential limitations (e.g., no results, performance) or confirm read-only nature, but for a count tool this is adequate.

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 only two sentences, front-loaded with the core purpose and example usage. No unnecessary words, and every sentence contributes to understanding what the tool does and when to use it.

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 three optional parameters and no output schema, the description explains the output includes counts and separate email/phone counts, and notes the live data source. This gives agents a complete understanding of expected results without missing key information.

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 already describes each parameter with 100% coverage. The description adds value by clarifying that 'industry' matches against both industry and category, and provides format examples for state (full name or code) and industry examples. This goes beyond the schema.

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 counts businesses with filters for industry, state, and city, and provides example queries like 'how many managed service providers are there in Texas'. This is a specific verb+resource plus scope, and it clearly distinguishes from siblings like search_businesses (which would return records, not counts).

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 explicitly says 'Use this to answer questions like...' and gives two concrete examples, which sets clear when-to-use context. It does not explicitly state when not to use, but the distinction from search_businesses is implied, making the guidance effective.

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, the description carries the transparency burden. It clearly discloses a key non-obvious behavior: contact details are never returned, only data-quality signals. It also frames the tool as showing 'what exists and how reachable it is,' which sets accurate expectations. It does not cover sampling determinism or auth, but the main boundary is explicit.

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, no filler. The first sentence front-loads the core purpose, and the second sentence adds crucial exclusionary context. 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?

Given four optional parameters and no output schema, the description covers the key aspects: what is returned (industry, location, data-quality signals) and what is explicitly not returned. It could explain the sampling behavior or the meaning of 'data-quality signals' in more depth, but it is sufficient for tool selection and basic 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%, so all four parameters are already documented. The description adds limited semantic value by tying the returned fields (industry, location, data-quality signals) to the parameters, but it does not introduce any additional parameter-specific guidance or syntax beyond the schema.

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 clear action and scope: 'Return a sample of matching businesses with industry, location and data-quality signals.' It also explicitly distinguishes itself from typical business-contact lookups by noting that contact details are never returned, which sets it apart from sibling tools like count_businesses and list_industries.

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 does not mention when to use this tool versus the sibling tools. It implies a discovery/sampling use case but provides no explicit guidance, exclusions, or alternatives such as 'use count_businesses for totals' or 'list_industries for valid industries.'

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