АЛИАС — арт-кондитерская, Санкт-Петербург

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

Annotations already declare readOnlyHint=true and openWorldHint=false, so the safety profile is covered. The description adds the content scope (what facts the page contains), but says nothing about return format, language, or currency of the data. With annotations doing the safety work, a 3 is appropriate.

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?

One content list plus a one-phrase call to action, front-loaded with the most important routing instruction at the end. Every phrase carries information; nothing is padding.

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 params and no output schema, the description must convey what comes back, and it does so by enumerating the topics covered. It could say more about the response shape (single text blob vs. structured fields), but it is sufficient for an agent to decide to 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?

Zero parameters, so there is nothing for the description to disambiguate — baseline 4 applies. No parameter claims are made or 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?

The description enumerates exactly what the tool returns — who we are, contacts, address, hours, rating, payment, delivery, how ordering works — which makes the resource concrete even without an explicit verb. It is clearly distinguishable from siblings like product_details or delivery_quote, though it doesn't name 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?

"Начните с этого" explicitly tells the agent to call this tool first, which is real routing guidance for an entry-point tool. It doesn't state when not to use it or name specific alternatives, so it stops short of 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?

Beyond the annotations, the description discloses two important behaviors: the booking is not immediately confirmed because a manager confirms the day and time, and personal-data consent must be collected before sending. That adds real context about the asynchronous, human-reviewed nature of the operation, which the annotations alone (idempotent, non-destructive, open-world) do not convey.

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?

Three short sentences, zero filler, with the core action front-loaded and the prerequisites (manager confirmation, consent) following immediately. Every sentence carries operational 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 an 11-parameter mutation tool with no output schema, the description covers the essentials: what is created, that confirmation is deferred to a manager, and the consent gate. It does not say how or when the customer learns of confirmation, but the rich schema compensates for most remaining gaps.

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 11 parameters are already documented in the schema, including consent, date formats, and the fillings limit. The description only reinforces the consent requirement and adds no format or dependency details beyond what the schema provides, 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 verb and resource ("Записать заказчика на дегустацию начинок"), which clearly identifies a booking action for a tasting. It is distinguishable from informational siblings such as tasting_info or list_fillings, though it never explicitly contrasts itself with submit_request, which may overlap.

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 verb — this is the tool for booking a tasting — and one prerequisite is stated: obtain the customer's consent before sending. However, no alternatives are named (e.g., when to use submit_request instead), and no exclusions or eligibility conditions are given.

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=true and openWorldHint=false, so the safety profile is covered. The description adds that the read returns two pieces of business information (acceptance and surcharge), which is useful, but it says nothing about behavior on unavailable dates or invalid input.

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?

One compact sentence that front-loads the two questions the tool answers, with no filler or repetition.

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-parameter read tool with full schema coverage and annotations covering safety, the description is nearly complete: it tells the agent what information comes back. Only minor gaps remain, such as behavior on closed dates or when the surcharge result should feed into a quoting 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% and the single date parameter already documents both accepted formats (ГГГГ-ММ-ДД or ДД.ММ.ГГГГ). The description adds no further meaning about the parameter, so the schema carries the load and 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 states a clear, specific purpose: it checks whether an order can be accepted for a given date and whether an urgency surcharge applies. It reveals what the tool returns (availability + surcharge), which goes beyond the name, though it never names a sibling or clarifies the boundaries with tools like delivery_quote or estimate_cake.

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 guidance on when to call this versus the many quoting/booking siblings (delivery_quote, estimate_cake, quote_gingerbread, book_tasting). The agent must infer that this is a pre-check step, with no exclusions or prerequisites stated.

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=true and openWorldHint=false, so safety is covered. The description adds that results carry prices, a small but real piece of return-content context, though it says nothing about catalog size, pagination, or whether items are filtered.

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?

One compact sentence with zero filler, and the resource and price detail are front-loaded. It is a noun phrase rather than a full clause, so the action is slightly less explicit than it could 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?

For a parameterless browse tool with read-only annotations and no output schema, the description covers the essentials. It is still thin on what the caller gets back (full list vs. categories, item fields beyond price), which matters for choosing it over search_products.

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 takes zero parameters, so there is nothing for the description to disambiguate; the baseline for a parameterless tool applies. The added note that output includes prices is a bonus rather than a parameter clarification.

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 concrete resource — cake decorations (figures, toppers, candles) — and adds that prices are included, which the bare name 'decor_list' does not convey. The listing verb is only implied by the name, and no sibling (e.g. product_details, search_products) is differentiated.

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 guidance on when to call this versus product_details or search_products, which appear to overlap in surfacing product information. The agent must infer that this is the full-catalog browse entry point.

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=true and openWorldHint=false, so safety and locality are covered. The description adds a useful behavioral detail — that omitting the address yields general conditions rather than an error. It does not indicate what the response contains (a price value, a range, terms), which is a modest gap.

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 short sentences, front-loaded with the core purpose and ending with the optional-parameter rule. Nothing is redundant and every clause carries 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 single optional-parameter, read-only lookup with no output schema, the description covers geography, input shape, and the no-address fallback. What's missing is any hint of the response form (fixed price vs. conditions), which an agent calling blindly might want.

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 schema already labels the address field. The description nevertheless adds real meaning: the address refers to a venue/house format, applies only within SPb/Leningrad region, and is optional with a defined fallback. That goes beyond the 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?

States a specific resource and outcome: delivery price by address (venue, house) within St. Petersburg and Leningrad region. This is clearly a read/lookup of a delivery cost rather than an ordering action. It doesn't explicitly distinguish itself from the closest sibling (quote_gingerbread), but the scope is unambiguous.

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?

"Без адреса — общие условия" gives a usable rule for when to omit the parameter, which is genuine conditional guidance. However, it says nothing about when to prefer this tool over siblings like quote_gingerbread or estimate_cake, leaving cross-tool selection 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 declare readOnlyHint=true and openWorldHint=false, so the safety profile is covered. The description adds one genuinely useful behavioral fact beyond the annotations and schema — that price per kg is independent of tier count and tiers are negotiated with the confectioner — but it does not say what the response contains or how firm/approximate the figure is beyond the title.

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 dense sentences, front-loaded with the calculation and followed by the tier caveat; nothing is padding. The colon-compressed style is efficient, though slightly telegraphic for a first-time reader.

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?

All eight parameters are fully documented in the schema and the tool is read-only, so the description covers the call itself. However, with no output schema, the description never indicates what an estimate result looks like (total price, breakdown, warnings), which is the main open question for a pricing 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 baseline is 3, and the description goes slightly beyond it by tying parameters to pricing mechanics: guests/weight set the mass, decoration sets the per-kg rate, fillings add a surcharge, and date drives urgency. That causal mapping is not stated in the schema, where date is merely 'the holiday date'.

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 the concrete computation: weight from guest count (or a supplied weight), price per kg by decoration, filling surcharge, and a date-driven urgency factor. That is a specific verb+resource that distinguishes it from the closest sibling quote_gingerbread by product, though the differentiation is implicit rather than stated.

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: an agent can infer this is the tool for a preliminary cake price, but the description never says when to call it instead of quote_gingerbread, list_fillings, or decor_list. The line about tiers being agreed with the confectioner hints at the boundary of what this tool answers, but no explicit when/when-not guidance is given.

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=true and openWorldHint=false, so the safety profile is covered. The description adds domain context—premium fillings, whole-cake-only constraint, and markups—but does not explain return behavior, pagination, or how the 'rules' are presented. No contradiction with annotations.

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 a single compact phrase with the resource front-loaded and no wasted words. It is slightly under-structured because it is a fragment without an explicit verb, but it is appropriately brief for a zero-parameter list tool.

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 zero-parameter read-only catalog with no output schema, the description gives enough domain vocabulary to know what data is involved. However, it never states that it returns a list or explains the filling rules/format, so an agent must infer the return shape from the name and annotations.

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 per the rubric. The description does not need to document parameters, and it does not introduce confusing parameter-like details.

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 identifies the resource (cake fillings) and some content attributes (premium, whole-cake only, markups), but it is a noun phrase with no action verb such as 'list' or 'return'. It does not explicitly distinguish this catalog from siblings like product_details or search_products, so the purpose is inferred largely from the tool name.

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 statement of when to use this tool, when not to use it, or which sibling to choose for related product information. The phrase implies it is a reference catalog, but no explicit usage 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 declare readOnlyHint=true and openWorldHint=false, so safety is covered. With no output schema, the enumeration of returned fields (price, min weight, Q&A) does add useful shape information, but the description says nothing about behavior on an invalid/unknown slug or whether all fields are always present.

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 returned data listed first and the parameter provenance last, so it is front-loaded and free of 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?

For a one-parameter read tool with no output schema, listing the content the caller receives plus the slug source is largely sufficient. Only error/absence behavior for an unmatched slug is 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?

Schema coverage is 100% and there is only one parameter, so the baseline is already satisfied. The description goes further by explaining where the slug value should come from (search_products), which is genuine semantics the schema does not provide.

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 the resource (specific product by slug) and enumerates the returned content: description, price, minimum weight/quantity, Q&A. This lets an agent distinguish it from search_products, though the action verb ('return details for') is only implied rather than stated.

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 parenthetical '(slug from search_products)' explicitly establishes the call chain, telling the agent this is a follow-up to search_products rather than a replacement. No explicit exclusions are given, but the workflow context is clear enough to guide selection.

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=true and openWorldHint=false, so the safety profile is covered; the description adds non-obvious behavior by explaining that the no-parameter call is a reference lookup while the parameterized call returns per-unit tier pricing and a packaged total. For a tool with no output schema, that return-shape disclosure is genuinely additive. It stops short of describing tier boundaries or any limits, hence not 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?

Three sentences, front-loaded with the mode split that matters most for invocation, with no filler. It is dense but each sentence carries information; there is no redundancy.

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 read-only pricing tool with five optional IDs and no output schema, the description supplies the essential return-shape and mode distinction so an agent can call it correctly in either form. Minor gaps (tier structure, how IDs are discovered) are acceptable given the fully described schema.

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 every parameter is already documented in the schema, which sets the baseline at 3. The description gestures at the same fields (sizes, painting, kinds, packaging, quantity) without adding format, ID-lookup, or default-override detail beyond what the schema states.

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 (gingerbread pricing) and its two modes: with no parameters it returns reference data (sizes, painting types, kinds, packaging), and with parameters it returns per-unit price at quantity tiers plus a total including packaging. This is more specific than the name/title alone and lets an agent distinguish it from catalog-style siblings, 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?

The description clearly says what each invocation mode yields (no params = reference lists, params = pricing), which tells an agent which form to call. It also gives a concrete scenario (wedding seating-name gingerbreads, usually standard size with simple/medium painting) and notes a manager collects the name list after the request, but it never rules out alternatives or names a sibling tool to use instead.

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=true and openWorldHint=false, so the safety profile is clear. The description adds useful context by identifying the source as blog articles and Q&A content rather than product or order data. It does not describe search behavior, result granularity, or limitations, but the read-only nature is already covered by annotations.

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 definition is a single compact sentence with no filler. It front-loads the content source and follows with examples that clarify supported topics. Every phrase contributes to understanding the tool.

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-parameter read-only retrieval tool, the description gives enough to understand the content source and topic coverage. However, it does not clarify routing against siblings such as search_products or submit_request, nor does it describe whether responses are snippets or full articles. No output schema exists, so return-value detail is not strictly required, but sibling differentiation is still 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?

The schema has 100% description coverage for the single required query parameter, defining it as a question or topic. The description offers example question types, which gives some semantic guidance, but it does not add format, scope, or phrasing constraints beyond the schema. Baseline 3 is appropriate because the schema already carries the parameter definition.

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 that the tool returns answers from blog articles and a Q&A section, which is a clear content source and outcome. It lists concrete topic examples such as cake quantity, filling choice, wedding gingerbread, and urgent orders, making the purpose recognizable. It does not explicitly differentiate this tool from siblings like about, search_products, or product_details.

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 examples imply that the tool should be used for general baking and cake questions drawn from editorial/FAQ content. However, there is no explicit when-to-use instruction, no exclusions, and no named alternatives for product search, pricing, or order submission. The agent must infer routing from the topic examples alone.

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=true and openWorldHint=false, so safety is covered; the description adds real value by disclosing the cap of 10 results, the returned fields (photo, price, link), and the automatic filter-relaxation fallback with an explanation of which conditions were loosened. It does not say whether relaxation can be suppressed or how results are ordered.

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?

Three short sentences, zero filler, with purpose stated first and the most surprising behavior (auto-relaxation) placed last where it is noticed before invocation.

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 responsibly covers the return shape (up to 10 items with photo, price, link) and the empty-result fallback. It leaves ranking/relevance order and whether filters are strictly applied on the relaxed pass unexplained, a minor gap for a 7-parameter search 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 description coverage is 100%, so every parameter (color, limit, query, tiers, section, occasion, max_price) is already documented in the schema. The description adds only the section vocabulary, which duplicates the enum, 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 concrete verb+resource (catalog search) and enumerates the catalog domains (cakes, gingerbread, cupcakes, desserts, gifts), which lets an agent separate it from product_details or estimate_cake. Scope and return intent are clear from the first sentence.

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?

Implies usage through the listed sections and the note that query should not duplicate section/occasion (those have their own fields). However, it never says when to prefer a sibling such as product_details or estimate_cake, nor does it state prerequisites or 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?

Adds real context beyond the annotations: nothing is charged, a manager calls back, and the exact price is quoted later — plus an explicit consent gate for personal data. Annotations already declare non-destructive and idempotent, and the description is consistent with both, but it says nothing about what the agent gets back on success or failure.

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?

Three short sentences, front-loaded with the action, then the disambiguation, then the precondition. No filler and nothing 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?

For a 14-parameter submission tool with rich schema descriptions and a declared expectation of a callback, the description covers the process and sets the right expectation. It could still say what a successful submission yields (request id, confirmation) since no output schema exists.

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 14 parameters including nested items fields are already documented in the schema. The description adds no additional meaning about formats, defaults, or which optional fields matter most, 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?

States a specific verb and resource: передать заявку менеджеру кондитерской, and clarifies it is not a payment or order confirmation. It never names which sibling handles a nearby case (e.g. book_tasting, estimate_cake), so an agent must infer the boundary itself.

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 clear preconditions (obtain the customer's consent before sending) and excludes two plausible misinterpretations (it is not payment, not order confirmation). It stops short of pointing to any alternative tool when the user actually wants a price estimate or a tasting.

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=true and openWorldHint=false, so the safety profile is covered without prose. The description adds the substantive behavioral context — that the returned content is procedural/price/enrollment information — but says nothing about format or whether the enrollment instruction is a link, phone number, or in-chat action.

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 that front-loads the topic and lists the covered content areas with no filler. Nothing in it is redundant with the title or 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?

For a zero-param, no-output-schema info tool this is close to sufficient, but it omits the one thing an agent most needs in this sibling set: whether this tool can complete a booking or only explains it. Given book_tasting and check_date exist alongside, that boundary should be stated.

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 takes zero parameters, so there is nothing for the description to disambiguate; baseline for a parameterless tool applies. The description adds no false parameter hints, which is the correct behavior here.

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 resource (tasting of fillings) and enumerates the three things it covers: how the tasting runs, the price, and how to sign up. An agent can tell it is informational rather than transactional, though the 'как записаться' phrasing sits close to the book_tasting sibling without naming it.

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 — an agent infers this answers pre-purchase questions about the tasting. There is no explicit 'use this when...' and critically no pointer telling the agent to use book_tasting rather than this tool when the user actually wants to reserve a slot.

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