Server Quality Checklist

Tool Scores

• get_product_offersA
  3.7/5.0
  Behavior4/5

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

  The annotations already declare readOnly, openWorld, idempotent, and non-destructive behavior, so the description focuses on additional behavior: it loads a public endpoint, deduplicates recommended and price-based offers, and labels club/application/quantity conditions. It adds meaningful processing context beyond the structured annotations, though it does not discuss edge cases or failure modes.

  Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

  Conciseness5/5

  Is the description appropriately sized, front-loaded, and free of redundancy?

  The entire description is one dense sentence that front-loads the action and resource, then adds two valuable behavioral notes about deduplication and condition marking. There is no filler, repetition, or unnecessary context.

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

  Completeness2/5

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

  The output schema and annotations cover return structure and safety, but with six parameters, zero required fields, and very low schema coverage, the description leaves significant ambiguity about invocation. In particular, it presents slug as part of the endpoint while the schema marks no parameters as required, which could confuse an agent about what must be supplied.

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

  Parameters2/5

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

  Schema description coverage is only 17%, with only validOn having a real description in the schema. The description hints at slug via the endpoint template and at condition labeling, but it does not explain stores, location, productUrl, excludeMembershipOnly, or how they affect results. It fails to compensate for the otherwise low schema coverage.

  Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

  Purpose5/5

  Does the description clearly state what the tool does and how it differs from similar tools?

  The description clearly identifies the action ('Načte' / loads), the resource ('veřejný detail /sleva/{slug}'), and the distinguishing post-processing behaviors: deduplicating offers and marking club, application, and quantity conditions. This makes it clearly distinct from siblings like search_products or compare_shopping_list, which are about discovery and comparison across products.

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

  Usage Guidelines3/5

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

  The description implies usage when a specific product slug is available and the agent needs its offers, but it does not explicitly state when to prefer this tool over search_products or compare_shopping_list. There is no mention of exclusions, prerequisites, or alternative routing.

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

• search_productsA
  4.1/5.0
  Behavior5/5

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

  The annotations already mark the tool read-only, idempotent, and non-destructive, and the description adds substantial behavior beyond that: it discloses the source type (public server-rendered HTML), default date semantics, automatic packaging-term separation, match.confidence/match.quality output fields, and the important caveat that results reflect data location, not stock availability. There is 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.

  Conciseness5/5

  Is the description appropriately sized, front-loaded, and free of redundancy?

  Five dense sentences are ordered well: search source, default date behavior, query handling, match metadata, and the key limitation. Each sentence earns its place and there is no repeated schema information or filler.

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

  Completeness4/5

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

  Despite 10 parameters and only 30% schema coverage, the description explains the tool's core behavior, defaults, query nuance, output metadata, and a crucial non-goal (stock confirmation); an output schema exists for return semantics. It falls short only on full parameter guidance and explicit sibling routing, but it is still largely complete for correct high-level use.

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

  Parameters2/5

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

  Schema description coverage is only 30%, so the description carries a heavy burden for parameter meaning. It adds some value by explaining that packaging details in the query are separated automatically, but it does not address limit, sortBy, stores, location, onlyOnSale, excludeMembershipOnly, or other parameters. Most parameter semantics remain undocumented in both the schema and the description.

  Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

  Purpose5/5

  Does the description clearly state what the tool does and how it differs from similar tools?

  The description names a concrete action ('Vyhledá nabídky') and a precise target ('veřejném server-renderovaném HTML Kupi.cz'), with the title further scoping it to 'akční produkty na Kupi.cz'. This clearly differentiates the tool from offer-lookup and shopping-list comparison siblings even without naming them.

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

  Usage Guidelines3/5

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

  The description provides useful behavioral caveats: it defaults to today's deals, does not present future deals as current, and is not a stock-availability confirmation. However, it never explicitly tells the agent when to choose search_products over get_product_offers or compare_shopping_list; usage is only implied through purpose and caveats.

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

• compare_shopping_listA
  4.6/5.0
  Behavior5/5

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

  Annotations already declare readOnly=true, openWorld=true, idempotent=true, and destructive=false. The description adds substantial behavioral detail: default date scope, package-count rounding, low-confidence products going to unresolvedItems, partial status with null totalPrice and resolvedPrice, and the approximation/distance/stock limitation. 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.

  Conciseness5/5

  Is the description appropriately sized, front-loaded, and free of redundancy?

  The description is front-loaded with the tool's purpose and then delivers compact behavioral rules: date default, quantity semantics, unresolved items, partial results, and limitations. Every sentence earns its place with no filler or redundancy.

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

  Completeness4/5

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

  For a 10-parameter tool with an output schema and strong annotations, the description covers the most important behaviors and caveats clearly. It still leaves some optional parameters like optimization modes, allowedStores/excludedStores, and requireCompleteBasket to schema-only documentation, and it does not explicitly route between this tool and its siblings.

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

  Parameters4/5

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

  Schema description coverage is only 30%, so the description must compensate. It meaningfully explains the critical amount+unit semantics with a concrete example and package-count recalculation, and it steers away from deprecated quantity/preferredPackage. It does not cover optimization, requireCompleteBasket, or store-filter parameters, but those are at least present in the schema.

  Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

  Purpose5/5

  Does the description clearly state what the tool does and how it differs from similar tools?

  The description states a specific verb and resource: comparing leaflet offers for multiple items while respecting stores, memberships, and maxStores. This clearly differentiates it from the sibling tools search_products and get_product_offers, which are individual product/offer lookups.

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

  Usage Guidelines4/5

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

  The description gives clear context: it is for multi-item basket optimization, defaults to today's valid actions, and explains quantity input via amount+unit. It does not explicitly name alternatives or say when not to use it, but the intended use case is evident.

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