AI Receptionist Rankings

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 non-redundant behavior: results are computed at fixed 100/200 calls-per-month tiers and the data comes from scraped vendor pages via a named source, which tells the agent the numbers are derived and third-party in origin.

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 the core operation and volume tiers before the attribute list, with the data-source note trailing as provenance. The attribute enumeration is dense but earns its place by previewing the comparison output; only the URL is arguably optional.

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 usefully enumerates what comes back (cost, billing model, plan limit, contract, trial, spam billing, integrations, score) and fixes the volume assumptions that shape those figures. Combined with the 100% schema coverage and full read-only annotations, an agent has everything needed to call and interpret 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% and the single parameter already documents 'Vendor ids or names', which the description repeats almost verbatim. The only mild addition is echoing the minItems=2 constraint as 'two or more'; the maxItems=6 cap is not mentioned. Baseline 3 is appropriate.

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 operation (side-by-side comparison) on a specific resource (AI receptionists) and enumerates the exact dimensions compared at two fixed volume tiers. This clearly distinguishes it from the singular get_vendor and from the ranking-oriented siblings get_rankings/list_rankings.

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?

'Two or more' plus 'side by side' implies the multi-vendor use case that separates it from get_vendor, but no sibling is named and no exclusion is stated. There is no explicit guidance on when to prefer this over fetching vendors individually or over the rankings tools.

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?

The annotations already declare read-only, idempotent, non-destructive, closed-world behavior, so the safety profile is covered. The description adds useful behavioral context by listing the returned vendor fields, stating the default ranking behavior, identifying where slugs come from, and noting the data source.

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 front-loaded with the core output description and follows with default behavior, parameter source, and data provenance. It is a bit dense because it enumerates many returned fields in one sentence, but the information is relevant and not repetitive.

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 one optional parameter, no output schema, and annotations covering safety, the description supplies the remaining essentials: what the tool returns, how the optional parameter is sourced, what happens by default, and where the data comes from. An agent has enough information to invoke 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?

Schema coverage is 100%, so the list parameter is already documented with an example and default. The description still adds value by explaining that the slug comes from list_rankings and that omitting it returns the overall ranking.

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 it returns a ranked list of AI receptionists with specific vendor data fields, so the resource and output content are unambiguous. It distinguishes itself from list_rankings by saying it accepts a slug from that tool, though it does not explicitly contrast with compare_vendors or get_vendor.

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 implies usage by saying the tool defaults to the overall list and accepts a slug from list_rankings, which helps an agent understand the main path. However, it does not explicitly say when to use this tool instead of compare_vendors or get_vendor, nor does it state 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?

Annotations already declare readOnly, idempotent, non-destructive, closed-world access, so safety is covered. The description adds genuinely new context: every returned field carries its source page and read date, and the optional calls parameter triggers a computed monthly cost — provenance and derived-value behavior the annotations cannot express.

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 and a source URL, with the field inventory and the input-format hint front-loaded. The first sentence is a long run-on list, but every clause names an actual returned attribute, so no sentence is wasted.

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 the returned facts plus their provenance. It stops just short of 5 by leaving cross-tool routing (versus compare_vendors/get_rankings) and unknown-vendor behavior unaddressed.

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 baseline is 3, but the description earns extra credit by clarifying that `vendor` accepts either an id or a name and giving concrete examples ("NextPhone", "Smith.ai", "Rosie"), plus confirming the calls parameter can be any volume, not just 100/200.

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 concrete resource — a single vendor's ranking facts — and enumerates exactly which facts (plans, cost at 100/200 calls, billing model, contract terms, trial, spam billing, integrations, score). The singular scope implicitly distinguishes it from compare_vendors, but no sibling is named explicitly.

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?

Usage is only implied: fetch this when you need details on one vendor. There is no explicit statement about when to prefer compare_vendors or get_rankings instead, and no prerequisites or failure behavior (e.g. what happens for an unknown vendor).

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?

The annotations already establish that this is a safe, read-only, idempotent, non-destructive operation in a closed world. The description adds useful scope and provenance by naming the site, the list categories, and the ordering information, though it does not describe pagination or exact response shape.

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 front-loaded with the core purpose and then enumerates the list categories efficiently. It is a single sentence with some redundancy from repeating the URL, but every element is relevant to what the tool returns.

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 discovery tool with no output schema, the description is complete enough: it says the tool returns every ranked list and includes ordering information. It does not spell out the exact response structure, but that is a minor gap given the tool's simplicity.

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?

There are zero parameters, so there is no parameter semantics to explain. The baseline score for a parameterless tool is 4, and no missing parameter documentation creates a gap.

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 gives a specific verb/resource: it lists every ranked list available on the site and enumerates the overall, vertical, and attribute categories. It is clear what the tool returns, but it does not explicitly distinguish itself from the sibling get_rankings tool, which likely retrieves rankings within a 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 description explains what the tool returns but gives no explicit guidance on when to use it versus compare_vendors, get_rankings, or get_vendor. An agent can infer that this is a discovery tool for available lists, but the routing decision is left implicit.

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