ReceptionistPrices.com pricing

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

Annotations already establish readOnly/idempotent/non-destructive/openWorld=false, so the remaining burden is light. The description adds real value beyond them: the pricing basis (3-minute calls), the data source and check date (provenance and freshness), and the exact set of figures returned.

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 returned figures, no filler sentences, and the source citation is short and justified by provenance needs. The first sentence is a dense four-item list that takes a second read, which keeps it from a 5.

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 range, median, per-minute all-in rate, and entry-plan range. Minor gaps remain: no statement of currency, units, or response shape, and 'prices checked October 26, 2026' is a future date that an agent may find ambiguous.

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, and the description earns one extra point by disclosing the measurement assumption behind `calls` (3-minute call duration), which the schema does not state. It also glosses `virtual` as live people, matching the enum description.

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?

Names the resource precisely (range and median monthly cost, all-in per-minute rate, entry-plan range) and scopes it to a category (AI vs live virtual receptionists) at a given call volume. An agent can distinguish this aggregate category view from get_vendor_pricing and compare_at_volume by inference, but the description never names those 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?

Usage is only implied: the word 'category' and the aggregate framing suggest this is the tool for a benchmark/summary rather than per-vendor detail, but there is no explicit when-to-use, when-not-to-use, or named alternative among compare_at_volume, get_vendor_pricing, or hire_vs_ai.

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 readOnlyHint, idempotentHint, destructiveHint=false and openWorldHint=false, so the safety profile is covered. Beyond that, the description adds genuinely useful behavior: results are sorted lowest first, quote-only vendors appear in a separate section, defaults are AI receptionists and 3-minute calls, and the data source and refresh date are disclosed. What is missing is return-shape or pagination detail, keeping it at a 4.

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 capability and ranking order are front-loaded, and the second sentence efficiently carries defaults and provenance. It is dense and single-paragraph but every clause adds information; the inline URL and check date are the only slightly clunky elements.

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 and a read-only comparison tool, the description covers what matters: what is computed, the pricing method, ordering, separate handling for quote-only vendors, defaults, and source freshness. An agent could call this correctly without further context.

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 67%, so the schema documents most parameters; the description reinforces two defaults ('AI receptionists, 3-minute calls') that map to type and minutes_per_call. The calls parameter, which is required and carries domain context in the schema, gets no additional explanation in the description. At moderate coverage this is a baseline 3.

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 resource and scope: monthly cost of every AI or virtual receptionist at a given call volume and call length, ranked lowest first. It also discloses the pricing methodology (cheapest published plan plus overage) and how quote-only vendors are handled, so an agent knows exactly what the output represents. It never names a sibling such as get_vendor_pricing to draw the contrast explicitly, which is why it stops short of a 5.

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 defaults tells the agent roughly when it applies, but there is no explicit when-to-use/when-not or named alternative (e.g., use get_vendor_pricing for a single vendor). The guidance is adequate but leaves comparisons with siblings to inference.

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, non-destructive, and closed-world behavior. The description adds meaningful context beyond annotations: it enumerates the returned pricing and terms fields, states the source, and includes a checked date for data freshness.

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 information-dense and front-loads the returned content before input examples and provenance. It is a long run-on enumeration, but every clause contributes relevant scope rather than 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 one parameter, no output schema, and strong read-only annotations, the description is largely complete because it explains the return content, accepted input style, and data provenance. It could add guidance on ambiguous vendor names or how to discover valid ids, but the essential calling context is 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?

The schema already documents the single 'vendor' parameter at 100% coverage. The description adds concrete examples such as 'Ruby', 'Smith.ai', 'Rosie', and 'RingCentral', making it clearer that either an id or a name is accepted.

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 defines the resource as one vendor's published pricing, overage, volume costs, billing terms and integrations, with URL and read date. It distinguishes the single-vendor scope from broader sibling tools by saying 'One vendor's,' though it does not explicitly name the alternatives.

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 can be retrieved but does not state when to use this tool versus siblings such as list_vendors or compare_at_volume. No explicit when-to-use, exclusions, or discovery guidance is provided.

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 cover safety (readOnly, idempotent, non-destructive, closed-world), yet the description still adds real value: it discloses the methodology (BLS OEWS median wage for SOC 43-4171, ECEC benefits excluding paid leave because those hours are already inside 2,080, NCS leave days, JOLTS quits times a replacement-cost share) and a price-check date. That lets the agent judge output trustworthiness, though it says nothing about units of the return value beyond 'monthly cost'.

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 purpose is front-loaded in the opening clause and every parenthetical (wage series, benefit exclusions, leave, replacement-cost share) earns its place as provenance. It is a single dense sentence whose length is justified but is harder to scan than it needs to be, and the URL could be trimmed.

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 zero required params, no output schema and read-only annotations, the description supplies the methodology and the comparative framing an agent needs to call it correctly. What is missing is any hint of the shape of the returned comparison (per-option cost figures, assumptions echoed back), which would help the agent interpret results.

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 interpretation: hours_per_week changes the model from a full-time hire to covering N hours a week, and the comparison is held at the same call volume. It does not explain the calls default of 150 or the effect of state on which wage series is used, so it is not a full 5.

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 precisely what the tool computes: monthly cost of an in-house receptionist versus the cheapest AI/virtual receptionist at the same call volume, with named data sources. It is unambiguous as a cost-comparison calculator, but it never names or distinguishes itself from siblings such as compare_at_volume or get_vendor_pricing.

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 or when-not guidance, and no alternative is named despite an obvious overlap with compare_at_volume and get_vendor_pricing. The only orientation is the implicit 'one full-time hire or N hours a week' mode, which is left for the agent to infer.

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 readOnlyHint, idempotentHint, and closed-world scope, so safety is covered. The description adds genuinely non-schema context: the meaning of 'Not documented' (absence in both vendor pages and the tool directory, not a guess) and a dated source URL, which tells the agent how to interpret and trust the result. It stops short of describing the response shape (e.g., how the integration page is returned).

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?

It is front-loaded with the scope and the tool list, but that eleven-name list duplicates the schema enum verbatim and consumes most of the text. The 'Not documented' and source sentences earn their place; the enumeration does not.

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 indicate what comes back, and it does: built-in vs Zapier, plus the page that shows it, along with a data-quality caveat and source date. Only the exact response structure and pagination/format are left unaddressed, which is a minor gap for a single-parameter read tool.

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 enum and its single 'The business tool' description already carry the mechanics. The description adds interpretable meaning by clarifying that the parameter is the business tool being integrated with, and that the subject of the report is the receptionist side — a distinction the bare enum values do not make.

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 and scope: for a named business tool, which AI/virtual receptionists have a built-in integration, which use Zapier, and the page that shows it. That is a concrete, checkable output, not a restatement of the name 'integrations'. It does not differentiate itself from siblings like get_vendor_pricing or list_vendors, which keeps it from a 5.

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 by the enumerated tool list: use this when you need integration status for one of those eleven platforms. There is no explicit statement of when to choose this over get_vendor_pricing or compare_at_volume, and no exclusion of cases (e.g., tools outside the enum).

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, closed-world behavior, so the safety profile is covered. The description adds genuinely useful hidden context: the data source, the 'prices checked October 26, 2026' freshness stamp, and that each record discloses whether prices are even published — all of which affect how an agent should trust the output. It just doesn't mention result volume or pagination.

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: the first front-loads what is returned, the second gives the purpose plus provenance (source, check date, URL). Slightly dense opening clause, but every element earns its place and nothing is repeated from the schema.

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 the shape of the response — and it does, naming the per-vendor fields and the data freshness. Combined with the enum-documented filter it is sufficient to invoke and interpret the call correctly; only pagination/result-size remains 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?

Schema coverage is 100% and the single `type` parameter carries its own enum value descriptions (ai / virtual / platform / all), so the schema does the work. The prose never mentions the type filter at all, leaving no added meaning beyond the structured field — the 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?

The description enumerates the exact resource (AI receptionists, virtual receptionists, build-your-own platforms priced by ReceptionistPrices.com) and lists the fields each row carries (entry plan, price-publication status, page-read date). This is unambiguous about what the tool returns, and 'Use it to find a vendor's id' implicitly contrasts it with get_vendor_pricing, though 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?

It gives a concrete usage cue — call this to obtain a vendor's id, presumably before calling a pricing/detail sibling. However, it never states when NOT to use it or names get_vendor_pricing / compare_at_volume as the alternatives for a caller who already has an id.

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