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

Annotations already provide readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the agent knows this is safe and non-mutating. The description adds value by disclosing error handling behavior: it lists how unknown ids (`not_found`) and malformed ids (`invalid`) are returned in the response. This goes beyond the annotations and is useful for the agent. It doesn't mention limits like pagination or timeouts, but given the output schema exists, this is sufficient. No contradiction found.

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 first line summarizes the core function, followed by a clear list of what's included per product, and ends with error handling notes. Every sentence earns its place, no fluff, and the most important constraint (up to 20, one city) is front-loaded.

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 (2 params, batch operation) and the presence of an output schema and comprehensive annotations, the description is complete. It covers input constraints, call semantics (one request), and error handling. The output schema presumably details the response structure, so the description doesn't need to. An agent has all necessary information to call this 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 100%, so the schema already documents both parameters (city and products) with detailed descriptions, including format and defaults. The tool description adds contextual value by clarifying that one request covers all products and mentions error fields, but it doesn't add new parameter-specific semantics beyond what's in the schema. Thus, a baseline 3 is appropriate; the description doesn't need to repeat schema 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 clearly states the action 'compare products' with the resource 'up to 20 products side by side for one city', and details the comparison dimensions. It distinguishes itself from siblings like get_product by implying batch comparison across multiple products, though it doesn't explicitly name a sibling. The specific fields listed (rating, prices, delivery, etc.) make the purpose 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?

The description specifies the scope (one city, up to 20 products) and input format (product ids, M-ids, URLs), which implies when to use it (batch comparison). It doesn't explicitly state when not to use it or name alternatives, but the context of sibling tools (e.g., get_product for single product) provides implicit guidance. A clear exclusion or mention of get_product would push to 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?

Annotations already cover read-only and idempotent behavior, so the description does not need to repeat that. It adds useful behavioral context by describing the item-level data included, the relationship to the new product id, and the fact that these items are not usually visible in plain search.

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-organized: it opens with the core purpose, then lists the per-item details, and closes with routing guidance. Every sentence contributes useful information without 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?

Given the rich input schema, output schema, and annotations, the description provides enough context for an agent to select and invoke the tool correctly. It covers the main use cases, the distinction between category and product modes, and the relationship to get_product.

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 parameters are already documented well. The description adds meaning by clarifying the two search modes (category vs. product copies) and by explaining how the M-id relates to get_product, which goes beyond the raw parameter descriptions.

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 that the tool finds markdown and used units in a city, organized by category or by copies of one product. It distinguishes itself from plain search and names get_product for store-level detail, so an agent can tell exactly what this tool is for.

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 explicitly says when to use this tool for markdown/used units and when to switch to get_product for store-level detail. It also notes that plain search rarely shows these items, reinforcing the appropriate context for this tool over alternatives.

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 the tool as read-only, idempotent, and non-destructive. The description goes beyond that by exposing conditional behavior: 'club price only when shown on the site', 'partner points (summary)', 'star split next to the variant-group rating', and 'markdown condition for used items'. These nuances are valuable and non-obvious.

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 a one-sentence summary, then expands into a dense list of card sections. It is long but every clause communicates a distinct element, and there is no filler. It could be more scannable with bullet points, but it remains efficient.

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 tool with a large output surface, the description enumerates nearly every section of the product card, including conditional pricing, variants, delivery, stores, and badges. The required parameter and city scope are covered by the schema, and an output schema exists. The only minor gap is explicit handling of not-found or invalid IDs.

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%, with city, product, include_specs, and include_stores already clearly documented. The description adds no parameter-level meaning beyond saying the fetch is 'for a city', so it does not 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 opening sentence 'Full live card of one product for a city' names a specific verb-resource pair with clear scope: one product, one city. The detailed field list further specifies exactly what the tool returns, making it easy to distinguish from sibling search tools like search_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 implies usage for comprehensive single-product lookup, but it never explicitly states when to choose this over siblings such as get_reviews or get_questions. The phrase 'one product' and the exhaustive card contents give clear context, but there is no direct alternative guidance.

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, and non-destructive behavior. The description adds meaningful behavioral details beyond that: answers are marked for expert/brand authorship and purchase status, and author names are never returned. This privacy constraint and marker behavior are valuable context not present in 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 compact and well-organized: the core resource and grouping are stated first, followed by useful behavioral details about markers and privacy. Every sentence earns its place, and there is no redundant repetition of schema or annotation 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 read-only tool with a rich output schema and well-documented parameters, the description covers the essential invocation context: what is returned, how it is grouped, and important privacy behavior. It does not explicitly state that results are scoped to the provided product, but the required product parameter and the nature of the tool make that sufficiently clear.

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 the schema already documents all four parameters. The description adds no parameter-specific meaning beyond what the schema provides, so the baseline 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 clearly identifies the resource as customer questions with their answers, grouped into threads, which distinguishes it from review or product tools in substance if not by explicit naming. It lacks an explicit verb like 'retrieve' and does not name sibling tools, so it falls 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?

The intended use is implied by the resource type: this tool returns customer Q&A for a product, and the required product parameter reinforces that. However, there is no explicit guidance about when to choose this over get_reviews 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?

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is covered. The description adds useful behavioral context: it returns both product and group ratings unfiltered, and it explains the YAMARKET source provenance. It does not mention pagination behavior beyond the schema, but the schema already documents page/limit semantics.

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 return-value inventory, then adds two clarifying notes. It is slightly dense but every sentence earns its place; no filler or repetition of schema details.

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 rich schema (100% coverage), annotations, and output schema, the description is largely complete. It explains the non-obvious YAMARKET source and the unfiltered rating behavior, which are the main gaps an agent would face. It could mention that `scope` affects the returned ratings, but the schema already covers scope semantics.

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 the schema already documents all six parameters thoroughly. The description adds a small amount of context (e.g., what `source` YAMARKET means, and that ratings are unfiltered), but it does not substantially expand on parameter semantics beyond the schema. 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 opens with a clear inventory of what the tool returns (date, stars, pros, cons, text, variant, source, votes) and explicitly names the resource (customer reviews). It also distinguishes itself by noting the product/group rating context and the YAMARKET source meaning, which helps separate it from sibling tools like get_questions or get_product.

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 the meaning of the `source` field and clarifies that the returned ratings are unfiltered, which helps an agent understand when to rely on them. It does not explicitly name sibling alternatives or state when not to use this tool, but the context signals and sibling list make the intended use fairly clear.

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 read-only, idempotent, and non-destructive. The description adds meaningful behavioral context by mentioning the one-week cache and by explaining the `delivery_area` semantics, which helps the agent interpret results correctly.

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, front-loaded with the core lookup purpose, and every sentence earns its place. It avoids repetition of schema details and annotations while still providing useful workflow context.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given a single documented parameter, an output schema, and rich annotations, the description covers everything needed: caching behavior, output field semantics, and how the result should be used downstream. Nothing essential is missing.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% and the name parameter already has a clear example. The description adds value by clarifying that the returned id is meant to be used as `city` in other tools, which goes beyond the schema and helps the agent connect the tool to the broader workflow.

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 action ('Look up Citilink city ids by name') and identifies the resource (Citilink's own directory). It also orients the agent toward how the result is used, clearly differentiating this lookup from the non-city sibling tools.

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 explicitly tells the agent to use the returned id as the `city` parameter in other tools, which is strong usage guidance. It does not explicitly state when not to use the tool, but the sibling tools are clearly unrelated and no alternative city-resolution tool exists, so the guidance is sufficient.

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 description goes well beyond the readOnly/idempotent/openWorld annotations by disclosing detailed output semantics: null regular price means not sold in the city, bonuses are not discounts, club price appears only when the site shows it, and total_found/has_more/categories/price_range_rub are all explained. No behavior contradicts 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 dense but well organized: one opening sentence covers query controls, followed by a structured block of per-item and response-level semantics. Every sentence adds necessary detail for a complex search tool, 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 9-parameter search tool with a rich output, the description covers query controls, per-item fields, pagination signals, category mapping, and the price-filter span. Schema and annotations handle parameter formats and safety, and the description adds the behavioral and output nuance needed to call the tool 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?

The schema already documents all 9 parameters at 100% coverage, so the baseline is 3. The description adds meaningful cross-parameter guidance: it ties the categories output to category_id, advises using a slug with find_markdown, and frames price_range_rub as a helper for setting price windows.

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 specific verb and resource ('Search Citilink') and enumerates the key dimensions: pages of 48, sorting, price window, category, and availability. This clearly identifies it as a product-search tool, though it does not explicitly distinguish itself from sibling tools like get_product or get_reviews.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

There is no explicit when-to-use guidance or exclusion of alternatives. The phrase 'Search Citilink like the site does' implies the primary use case, but with six siblings such as get_product, compare_products, and get_reviews, the description never tells the agent when to prefer this tool over those.

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