Vargov®Design

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

Annotations already cover the read-only/idempotent safety profile, yet the description adds substantial context: matching is word-based rather than semantic, up to three candidates ranked by word match, and found=false only under a low word-overlap condition — all behavior an agent could not infer from structured fields.

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 resource and topic scope, then the matching and quoting rules. It is one dense paragraph and the long topic enumeration is partly expendable, but nearly every sentence carries operational guidance.

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 explains the return shape (up to three ranked candidates with their source pages, plus a found flag) and the quoting rule, so an agent knows exactly how to interpret and use 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%, so the baseline applies, but the description still adds meaning: lang sets the answer language with English as fallback where no translation exists, and the question should preferably be English or Russian.

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 ('Published answers of Vargov®Design') and enumerates the covered topics (ordering, delivery, warranty, BIM/IFC, etc.), which makes it clearly distinct from siblings like get_composition or configurator_link.

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 conditions: use for published brand answers, quote only if the candidate actually answers, and fall back to contact links for a manager when nothing fits. It also notes English/Russian questions work best, but never names a sibling tool as the alternative.

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 readOnly/idempotent/non-destructive/closed-world, so the description's burden is light. It still adds real behavioral context beyond the annotations: dimensions are in millimetres, height applies to the 'element cloud' and is optional, and compositions not built to room dimensions open without them (calculation: individual or not_by_room_size). That last caveat is genuinely non-obvious and worth the space.

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 core purpose, but the second is a dense run-on mixing LC code behavior, room-dimension fallback, and internal jargon ('calculation: individual or not_by_room_size'). The internal term is unexplained and costs readability without clearly helping an agent decide or call correctly.

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 5 parameters, full schema coverage, and no output schema, the description covers inputs adequately but never states what the tool returns (presumably a URL string). For a link-builder with no output schema, that return-value shape is the one missing piece an agent would want confirmed.

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 length_mm, width_mm, height_mm, code, and lang with ranges and units. The description reinforces units and the optionality of height, but says nothing about the LC code format or the lang parameter, so it does not materially exceed the schema baseline.

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 opens with a concrete noun+verb framing: 'A link that opens the Vargov®Design 3D configurator with the zone dimensions already filled in.' That is a specific resource (a pre-filled configurator link) rather than a restatement of the name. It does not differentiate itself from siblings like get_composition or search_compositions, so 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?

There is no explicit 'use this when' statement, no prerequisites, and no mention of the sibling tools it competes with. The clause about LC codes describes a conditional behavior of the link rather than guidance on when an agent should call this tool versus get_composition or search_compositions.

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 this as a safe, read-only, idempotent lookup. The description adds useful scope detail by enumerating exactly what is returned: dealer regions and contacts, showroom address, daily hours, phone, map links, and direct contacts. It does not contradict the 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 description is a single efficient sentence that front-loads the main resource and then lists the relevant subcomponents. There is no redundant or filler text.

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 read-only lookup with full schema coverage, complete annotations, and no output schema, the description is largely complete because it enumerates the returned content. The main omission is guidance on when this should be used instead of ask_brand or other siblings.

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 is one optional lang parameter and schema description coverage is 100%, so the schema already fully documents the allowed languages and default. The description adds no additional parameter semantics beyond that baseline.

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 identifies the resource and its contents: official dealers with regions/contacts, the Moscow showroom details, and direct brand contacts. It does not name an action verb or explicitly distinguish itself from sibling tools such as ask_brand, but the subject matter is specific enough to be identifiable.

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 gives no explicit guidance on when to use this tool versus alternatives like ask_brand. Usage is only implied by the listed content, with no exclusions or routing conditions.

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 read-only, idempotent, closed-world behavior, so the description's main added value is the null-field semantics ('a null field means the brand does not state it, not that the answer is no') and the explicit absence of prices. That is useful behavioral context beyond the annotations, though it does not discuss error handling for an unknown code.

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 purpose is front-loaded and the two caveat sentences are valuable. The middle field list is long and dense but each item describes a returned field, so it largely earns its place; a bulleted or more structured presentation would improve readability.

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 does the necessary work of enumerating returned fields and explaining null values and missing price data. It stops short of describing error behavior for an invalid LC code or exactly what 'where it works' and 'elements in stock' contain, but it is substantially complete for this read-only lookup.

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 both the code formats and the language enum are already fully documented in the schema. The description adds little parameter-specific meaning beyond repeating that lookup is by LC code, so the 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?

The description states a specific verb and resource ('Facts about one composition by its LC code') and enumerates the exact fields returned. It is clearly a single-item lookup, which distinguishes it from search_compositions even though the sibling is not named.

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 'one composition by its LC code' and the required code parameter. However, the description never explicitly says when to use this instead of search_compositions or ask_brand, nor does it mention prerequisites such as obtaining a valid LC code first.

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 description correctly spends its words elsewhere: it discloses the return shape (LC codes, page links, cover images) and the ranking behavior (best matches first, brand examples first). That ordering note is real behavioral information not present in any structured field.

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 search capability, with the alternative-route sentence at the end where it belongs. The motif enumeration (butterflies, stingrays, birds, droplets...) is somewhat decorative but does teach the expected vocabulary.

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, so the description carries the burden of explaining return values and ranking — and it does so. All four parameters are covered by schema text, and the agent knows what it gets back and in what order.

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 genuine value by clarifying the 'and/or' combinatorics between motif and product type and by emphasizing that motif works across any of the eight site languages. Those are semantic additions beyond the enum and per-parameter 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 verb and resource (find Vargov®Design compositions) plus the two search axes (element motif and/or product type). It explicitly names the sibling get_composition as the alternative for a different need, so an agent can route without opening either schema.

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?

Clear usage context: use this for discovery, use get_composition for facts about one composition. There are no exclusions against the other siblings (ask_brand, configurator_link), but the primary fork is spelled out explicitly.

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