PublicLeads

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

Annotations already declare readOnly, idempotent, non-destructive, and closed-world, so safety is covered. The description adds genuinely useful behavioral context beyond that: the sample is capped at 5 rows and emails/phones are masked, which tells the agent not to expect real contact data. It doesn't mention rate limits or pagination, but for a single-record read that is minor.

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 payload (what you get back) is front-loaded in the first sentence, with the input instruction in a short second sentence. Every clause carries information — field list, price, freshness, sample size, masking — with no filler.

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?

There is no output schema, but the description compensates by enumerating the returned contents and the masking of PII. With one required parameter and complete annotation coverage, an agent has everything needed to call this correctly and interpret the result.

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 coverage is 100% and the single parameter is fully documented in the schema. The description still adds meaning by naming the concrete host (publicleads.com) for the URL form and tying the value back to search_lists output, which the schema only calls 'the list page URL'.

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?

States a specific verb (get details) and resource (one list from search_lists), then enumerates exactly what is returned: fields, price, freshness policy, and a masked 5-row sample. It explicitly anchors the input to the sibling search_lists, so an agent can tell it apart from search_lists, get_market_stats, and request_custom_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?

The second sentence gives a clear invocation path: pass the 'list' value that search_lists returned, or the list's URL. This implicitly establishes the search_lists → get_list_details workflow. It stops short of stating when not to use it (e.g., versus request_custom_list for lists that don't exist), so it earns a 4 rather than a 5.

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?

Annotations already declare read-only, idempotent, non-destructive, closed-world behavior, so the description's contribution is what the aggregation returns and that it deliberately omits contact details. It stops short of noting data-freshness, coverage limits, or how percentage denominators are computed.

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, front-loaded with the resource and metric list, then the usage example. Nothing is repeated from the title or schema and no sentence is filler.

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 carries the return-value burden and does so by enumerating every reported metric and the exclusion of contact fields. Combined with annotations covering safety and the fully documented schema, an agent has everything needed to select and call it.

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 coverage is 100%: the trade field already points to list_trades slugs and the location field already specifies US/Canada/Australia. The description's Ohio/plumbers example implicitly maps to the two parameters but adds no syntax, format, or matching behavior beyond the schema, so baseline 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?

States a specific verb and resource ('Aggregate statistics for an industry in a place') and enumerates exactly what is computed: counts, average rating, review totals, and per-field coverage percentages. The line 'No contact details' plus the market-sizing framing separates it from the sibling list/search tools that return records.

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?

Gives an explicit when-to-use condition with a concrete example ('how many plumbers are there in Ohio'), which anchors the tool to market-sizing questions. It does not name the alternative siblings (e.g., get_list_details) or state when-not to use it, so routing is implied rather than prescribed.

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?

Annotations already declare readOnly, idempotent, non-destructive, and closed-world, so the safety profile is covered. The description adds value beyond that by disclosing the return content (industry slugs) and the country coverage (US, Canada, Australia), which tells the agent what data exists before calling.

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?

A single sentence with the payload described first and the call condition last — front-loaded and free of filler. Nothing in it is redundant with the annotations.

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?

No output schema exists, yet the description still tells the agent what comes back (industries plus slugs plus countries) and how to use it downstream. For a zero-parameter lookup with full annotation coverage, nothing needed is missing.

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?

Zero parameters, so there is nothing to document and the baseline is 4. The description instead documents the response shape, which is the useful information for a no-arg tool.

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?

States a specific verb and resource ('List every industry (trade)') and disambiguates the name mismatch between list_trades and 'industries.' It also names the concrete return payload (slug for search_lists) and country scope, so an agent knows exactly what it gets and how it feeds a sibling tool.

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?

Gives an explicit trigger — call it when the user's industry may not match a name directly — which is a real disambiguation cue against going straight to search_lists. It stops short of naming search_lists as the alternative path or stating when not to call, so not a full 5.

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?

Annotations already establish that the operation is read-only, idempotent, non-destructive, and closed-world. The description adds meaningful workflow context: it returns a link and price, the data is measured, and the real count is supplied before any payment, which tells the agent this is a no-commitment inquiry.

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 tightly constructed sentences front-load the trigger condition and then explain the response and payment flow. There is no filler, and each sentence contributes necessary routing or behavioral information.

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 one-optional-parameter tool with no output schema, the description is complete: it explains when to use it, what it returns (link and price), and the no-payment-until-measured flow. An agent has enough to call 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 single optional description parameter is fully documented in the schema, including its 300-character limit and demand-signal logging behavior. The description adds no additional parameter guidance, 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 outcome—returning a custom-list request link and its price—and defines the triggering situation: when no ready-made list fits, with concrete examples like an unusual industry, county, radius, or combined industries. This clearly distinguishes it from search_lists and the other list-detail siblings.

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 a clear condition for use: when no ready-made list fits, including several illustrative edge cases. The alternative is implied as ready-made list search rather than named explicitly, so it falls just short of fully explicit when/when-not routing.

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?

Annotations already establish read-only, idempotent, closed-world behavior, and the description adds real value on top: a hard cap of 10 results, the ranking precedence (exact city, then state/province, then country), loose spelling matching, and the exact fields returned. It stops short of saying what happens when nothing matches or how to interpret a zero-result response.

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?

Front-loaded with purpose, then result shape, then input formats, then trigger — a logical order with no filler. The return-field enumeration is dense but each item earns its place by preempting a follow-up call.

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 carries the return contract and does so thoroughly (fields, ordering, cap). The only material gap is edge-case behavior: empty results, and whether the agent should fall back to request_custom_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?

Schema coverage is 100%, so the baseline is 3; the description goes slightly beyond by spelling out accepted location granularities (city, state/province, country) and disclosing that spelling is matched loosely — a behavior not captured in the schema. Trade semantics are largely duplicated from the schema's list_trades reference.

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?

States a specific verb and resource ('Find ready-made business contact lists') plus the dimensions searched (industry, location). It is clearly distinguishable from siblings such as get_list_details (per-list lookup) and request_custom_list (bespoke lists).

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?

Ends with a concrete triggering scenario ('when someone needs leads or prospects of a type of local business'), which gives an agent a clear use condition. However, it never names the alternatives — notably request_custom_list when no ready-made list matches — so the routing is left implicit.

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