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

Annotations already establish read-only, idempotent, non-destructive behavior. The description adds valuable behavioral detail: results are live, up to 50 articles can be compared per request, and articles without an offer are listed with a reason, which helps set expectations for edge cases.

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 compact and front-loaded: the first sentence states the core capability and limit, and the following list enumerates returned fields without fluff. No unnecessary repetition of schema or annotation data.

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 a single well-documented parameter, rich annotations, and an output schema present, the description covers the core behavior, batch limit, and edge case of unavailable articles. Nothing essential for selecting or invoking the tool 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?

The single parameter is fully described in the schema ('Article numbers to compare') with min/max constraints, so the description adds little about parameter syntax. It reinforces that the parameter accepts article numbers and supports up to 50, but does not need to compensate for a schema 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 clearly states the tool performs live side-by-side comparison of offers for up to 50 articles, and enumerates the offer fields returned. This distinguishes it from single-product get_product, search, price history, and reviews siblings by emphasizing multi-article comparison in one request.

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 conveys the intended use case—comparing current offers across multiple articles—by specifying batch size and included data such as price, stock, seller, and delivery estimate. It does not explicitly name alternatives or state when not to use it, but the context is unambiguous.

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, openWorldHint=true, idempotentHint=true, and destructiveHint=false. The description adds meaningful behavioral details beyond those: weeks without data are absent, each point is a weekly average stamped with a date, and the last point is not the current price. This is exactly the kind of context annotations don't provide, so it earns 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 description is compact and well-structured: the main purpose is stated first, followed by two clarifying sentences that add behavioral constraints. There is zero redundancy, and every sentence earns its place. It's appropriately sized for the tool's simplicity.

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 that an output schema exists (Has output schema: true), the description needn't explain return values. It covers all essential operational aspects: data granularity (weekly), absence handling, current price distinction, and trend validity. For a read-only tool with one parameter, this is complete and leaves no ambiguity for correct invocation.

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 full description coverage for the single parameter 'article' (100%), including an example value and type. The description doesn't add further parameter semantics, but with high coverage the baseline is 3 and there is no gap to compensate. The description's mention of 'article' is consistent, but it doesn't enrich beyond the schema.

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 the tool's purpose: it returns weekly average prices for an article, and contrasts with the live price. This distinguishes it from siblings like get_product or get_reviews, which serve different resources. The verb 'get' and resource 'price history' are specific.

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 explicit usage guidance: it warns that the last point is not the current price and directs the agent to `current_price_rub` instead, and cautions that with 0–1 points no trend can be claimed. It doesn't explicitly list alternative tools for when to use it, but the context is clear enough given the sibling names.

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 that the tool is read-only, idempotent, and non-destructive. The description adds useful behavioral context by calling the data 'live' and exposing that variant inclusion is optional, which complements openWorldHint by signaling that prices and stock can change between calls. No contradiction with annotations exists.

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, then delivers a dense but purposeful list of return contents in one well-structured sentence. It is longer than minimal, but nearly every clause contributes useful information for tool selection and 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?

Given the read-only annotations, full parameter documentation, and presence of an output schema, the description is complete for calling the tool correctly. It clarifies the granularity (one article), the live nature of data, and the optional group-variant behavior, so no essential contextual gap remains.

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 input schema already documents both parameters fully, including the article number format and the meaning of include_variants. The description's 'optionally — the other articles merged into the same product group' phrase mirrors include_variants but does not add new parameter-level semantics beyond the schema.

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 operation and resource: it returns a 'Full, live product card for one article'. The detailed list of return contents (price, stock, delivery, seller, ratings, etc.) makes its purpose concrete and distinguishes it from broader tools like search_products and compare_products.

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 frames the tool as the single-article detail card, which is enough context for an agent to select it over search or compare tools. It also notes the optional include_variants behavior. However, it does not explicitly direct the agent to siblings such as get_price_history for full price history or get_reviews for review detail.

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 mark this as read-only, idempotent, and non-destructive, so the safety profile is covered. The description adds valuable behavioral context beyond annotations by explaining that the tool returns both the star split from the feed and the merged group's rating, and that the feed may contain fewer ratings than the card counts. This nuance about potential data discrepancies is exactly the kind of behavior an agent needs to know, making it a strong supplement to 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 two short paragraphs. The first sentence immediately states the core function and output fields. The second paragraph adds a crucial clarification about rating sources and discrepancies in a compact, front-loaded manner. Every sentence earns its place, with no redundancy or filler. This is an excellent example of concise, structured documentation.

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 the tool's moderate complexity (6 parameters, enums, and a subtle rating nuance), the description is thorough. It explains the output fields, the star-split and group-rating distinction, and the difference from get_product's card rating. The output schema exists, so return format details are covered there. The only minor gap is not explicitly mentioning how pagination or limit works, but the schema's limit parameter and the tool's read-only nature make that less critical. Overall, it's complete enough for an agent to call 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 description coverage is 67% (4 of 6 parameters have descriptions). The description itself does not elaborate on any input parameters—it only lists output fields. For the two parameters lacking schema descriptions (max_rating, min_rating), the description offers no additional clarity, though their constraints are self-explanatory. Since the schema covers most parameters and the description adds no parameter-specific meaning, a baseline score of 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 opens with a precise statement of the tool's function: 'Buyer reviews with date, stars, text, pros, cons, bought variant and seller reply.' It clearly names the resource (reviews for an article) and the specific data fields returned. It also distinguishes itself from the sibling get_product by explaining that the card rating (from get_product) is the one shown on the site, which disambiguates which rating a headline refers to. This makes the purpose unmistakable.

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 provides clear context about when to use this tool versus get_product: it explicitly states that the feed may hold fewer ratings than card counts and that get_product provides the site-displayed rating. This gives the agent a concrete reason to choose one tool over the other for rating-related queries. However, it does not explicitly state 'use this tool when you need detailed review text' or list other exclusions, so it's slightly shy of a perfect score.

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 mark this as read-only, idempotent, and non-destructive, so the bar for additional disclosure is lower. The description adds meaningful behavioral detail: missing fields are omitted, in_stock_qty 0 means sold out, total_found and has_more have specific meanings, and search prices are from the result feed and should be confirmed with get_product. No contradiction with annotations exists.

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 compact and front-loaded with the core behavior, then adds only high-value detail about result semantics and the companion get_product tool. Every sentence earns its place, and the structure makes the most important facts easy to find.

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 search tool, this description is complete: it explains the search behavior, the meaning of key result fields, pagination semantics, missing-data behavior, and the price-feed caveat. Together with the rich input schema and annotations, an agent has enough context to invoke the tool correctly and interpret results appropriately.

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%, and the input schema already explains query, sort, page, limit, price_min, and price_max clearly. The description adds only a high-level mention of sorting and a price window, which does not materially enrich parameter understanding beyond the schema. This matches the baseline expected for full schema coverage.

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 tool as a search against Wildberries with real pages, sorting, and a price window, which distinguishes it from siblings like get_product. The return fields are enumerated, so an agent knows exactly what kind of results to expect. This goes well beyond 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?

The description clearly positions this tool for product discovery and explicitly points to get_product for confirming finalists, which gives an agent a concrete routing cue. It does not exhaustively list when not to use it versus get_price_history, get_reviews, or compare_products, but the context is still clear and useful.

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