AnsweringPrices.com pricing

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

Annotations already declare readOnly, idempotent, non-destructive, closed-world behavior, so the description's job is to add context beyond that. It does: it discloses data provenance (AnsweringPrices.com), a freshness date, the specific metrics returned (all-in per-minute rate, published overage range, range endpoints), and the special AnswerAmerica prepaid/rollover pricing basis. That is meaningful behavioral context the annotations do not carry.

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 opening sentence is front-loaded with purpose but is a long, comma-heavy run-on that bundles returned metrics, call-length assumptions, overage ranges, and range endpoints together. The source citation and date are useful but add length; the description is adequate rather than crisp.

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 must convey return content, and it does list the metrics returned (range, median, per-minute rate, overage range, range endpoints). Provenance and pricing-basis context are also provided. The only gap is that it does not describe the shape/format of the result or how the range endpoints relate to categories.

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, but the description adds value by clarifying that call volume is modeled on 3-minute calls and that the category determines which narrower service set bounds the range. The parameters' practical meaning (monthly calls at a defined call length) is enriched beyond the bare schema labels.

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 resource (price range and median for live, AI, or hybrid answering services at a given call volume) and enumerates the returned metrics. It is clear what the tool computes, but it never names or contrasts with siblings such as compare_at_volume or get_vendor_pricing, so sibling differentiation is absent.

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?

There is no explicit when-to-use guidance and no mention of alternatives. An agent cannot tell from the description why it should call category_range rather than compare_at_volume or get_vendor_pricing, nor under what conditions this tool is inappropriate.

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?

Read-only/idempotent semantics are already in the annotations, so the description doesn't need to repeat them. It adds real context beyond them: sorting order (lowest first), how AnswerAmerica's prepaid credits are priced as a minimum upfront package, that quote-only vendors are grouped separately, and vendor-specific caveats (message-only, law firms only). The data source and freshness date are also 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?

Purpose and sort order are front-loaded, followed by vendor caveats and finally provenance. It is one dense block with no wasted sentences, though the run-on structure and last-sentence URL make it slightly harder to scan than a broken-out list would be.

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 still communicates the ordering and grouping of results and the special-case vendor handling, which is what an agent needs to interpret output. It stops short of describing the returned fields or units, but the essentials for correct invocation and interpretation are present.

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, including defaults for category and minutes_per_call. The description restates those defaults and clarifies that cost is computed jointly from volume and call length, but adds no parameter semantics the schema lacks (e.g., vendor id format, valid vendor names).

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 tool performs a specific action (price comparison) on a specific resource (answering services) with a clear scope: cost at a given call volume and call length, sorted lowest first. It is distinguishable in practice from 'category_range', 'get_vendor_pricing' and 'list_vendors', but never explicitly contrasts itself with them.

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 implied rather than stated: 'at a call volume and call length' plus the default assumptions (live services, 3-minute calls) tell an agent roughly when this applies. There is no explicit 'use this when... / use X instead when...' guidance relative to the sibling 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?

Annotations already declare readOnly/ idempotent / non-destructive / closed-world, so the safety profile is covered. The description adds real context beyond that: provenance (AnsweringPrices.com), a read date (October 26, 2026), per-field URL and read-date attribution, and the fact that terms are summarized from the vendor's own terms page — useful for judging freshness and trust.

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 core content is front-loaded, but the AnswerAmerica sentence about upfront prepaid packages and credit rollover reads as a dangling vendor-specific note with no clear role, and the source/freshness trailer duplicates info loosely. Tightening would improve 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?

For a one-parameter read-only lookup with no output schema, the description covers inputs, the shape of the returned data (plans, overage, comparison cost at 50/100/200 calls, billing terms, per-item URL and date), and provenance. Only the guidance on when to reach for siblings 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?

Schema coverage is 100%, so the schema already documents the single 'vendor' parameter. The description still earns credit by clarifying that an id OR a name is accepted and giving concrete example values ("PATLive", "Ruby", "VoiceNation"), which is more actionable than the bare schema text.

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 names a specific verb+resource (one vendor's published plans, overage, comparison costs and billing terms) and enumerates the returned fields. An agent can distinguish it from list_vendors (plural listing) and compare_at_volume (cross-vendor volume comparison) by the 'one answering service' scoping, though it never names the siblings 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?

Single-vendor lookup is implied by 'One answering service's published plans' and the accepted-id-or-name note, but there is no explicit when-to-use/when-not or pointer to list_vendors for discovering ids or compare_at_volume for cross-vendor pricing. The agent must infer the 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 declare readOnly, idempotent, non-destructive and closed-world, so safety is covered. The description adds genuinely new context beyond that: the data source, the freshness date ('prices checked October 26, 2026'), and what each row contains. It omits ordering and any pagination/result-size behavior, which keeps it from a 5.

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 returned-field inventory leads, followed immediately by the use case, with provenance (source URL, check date) last. The source URL plus date is the only mildly verbose element, though it does earn its place as data-freshness context.

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 correctly carries the return-value burden by listing the per-vendor fields an agent will get. For a single optional enum parameter tool this is nearly complete; only result ordering and whether the list is exhaustive vs. paginated are left unstated.

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 'category' parameter is fully documented in the schema (100% coverage), so the baseline is 3. The description reinforces the enum semantics by naming 'live, AI and hybrid' as the dimensions the index is sliced by, giving a small but real lift over the schema alone.

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 exactly what the resource is (every answering service indexed by AnsweringPrices.com) and enumerates the returned fields: entry plan, price-publication flag, and pricing-page read date. It also implicitly separates it from get_vendor_pricing by framing the tool's job as 'find a vendor's id'.

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 one explicit use case ('Use it to find a vendor's id'), which tells the agent when to reach for it over fetching pricing for a known vendor. It stops short of naming the alternative siblings (get_vendor_pricing, compare_at_volume) or stating exclusions, so it falls just under the top band.

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