puhy.cz – katalog elektroniky a nářadí

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

Annotations already cover the safety profile (readOnly, idempotent, non-destructive, closed world), so the description's added value is the workflow context: the cart_url opens puhy.cz in the user's browser where the user completes checkout and payment, and successive cart_urls add to the same cart. That is meaningful context not present in the annotations. The tension between readOnlyHint=true and 'adds its items to the same cart' is real but plausibly browser-side rather than a server mutation, so it is a caveat rather than a contradiction.

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 returned payload, then the cart_url workflow, then the usage fit and limit. No filler, though the long run-on enumeration in sentence one is dense and could be split for scanability.

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 enumerates return fields, and it adds the 50-line limit, the cart accumulation rule, and where payment happens. What is missing is partial-availability behavior (e.g., whether unavailable items still appear) and how the goods total and shipping options relate across the 50-line cap.

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 0%, so the description must carry the burden, and it partly does: "list of items with quantities" maps to the items array and "up to 50 lines per call" explains the maxItems cap. It says nothing about product_id format/validity or what happens to lines whose requested quantity exceeds stock, leaving the nested fields documented only by their names.

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 ("Check a list of items with quantities") and enumerates exactly what comes back: orderable quantity, price, stock, delivery time, goods total, shipping options, and cart_url. It implicitly separates itself from get_product and search_products by working on a multi-line list, but it never names those siblings 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?

"Suited to BOMs / parts lists: up to 50 lines per call" gives a clear usage context, and the note that each cart_url accumulates into the same cart is useful. However, there is no explicit when-not guidance and no routing to get_product for a single item or get_shipping_and_payment for shipping-only lookups, so the agent must infer the boundaries.

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 read-only, idempotent, non-destructive, closed-world lookup. The description adds useful behavioral detail beyond annotations, specifically that quantity causes package-size rounding and a line-total calculation, though it does not describe error behavior for invalid or missing identifiers.

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 lookup behavior, then adds the quantity effect. It is dense but efficient, with no obvious filler, though the phrase 'minimum order / package rules' is slightly compressed.

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?

The tool has three parameters, no output schema, and annotations that cover safety. The description lists returned data and quantity behavior, which is helpful, but it omits the important precondition that at least one of product_id or EAN should be supplied, and gives no error 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 0%, so the description must carry parameter meaning. It explains that product_id and EAN are alternative lookup keys and that quantity affects orderable quantity and line total, but it does not clarify whether at least one identifier is required, what happens if both are supplied, or EAN format expectations.

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/resource: retrieve product detail by product_id or EAN, and lists the fields returned. It is clear what the tool does, though it does not explicitly distinguish itself from search_products or check_availability.

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: use this for a known product_id or EAN, and optionally pass quantity for orderable quantity and line total. However, there is no explicit when-to-use guidance versus search_products or check_availability, and no exclusion rules.

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 establish readOnly, idempotent, non-destructive, and closed-world, so the safety profile is covered. The description adds scope context (CZ/SK region, EU reverse charge) but never says whether these are static published policies or live quotes, nor anything about freshness or return shape. With annotations carrying the behavioral load, 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?

A single dense fragment with no filler, and the most commonly needed content (carriers and prices) is front-loaded. The parentheticals are compact, though the whole thing reads as a noun list rather than a sentence, which slightly weakens orientation.

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 reference tool with no output schema, the content inventory is a good proxy for what comes back, and annotations cover safety. The one meaningful omission is whether the data is static policy versus live carrier rates, which affects whether the agent should re-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?

The tool takes zero parameters, so there is nothing for the description to disambiguate; the empty schema is self-explanatory. Baseline 4 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?

Enumerates concrete resources: shipping carriers and prices for CZ/SK, the free-shipping threshold, payment methods, the COD fee, B2B EU reverse-charge invoicing, and terms/contact links. That is specific enough to separate it from the sibling tools, which all deal with products and availability. It lacks an explicit verb ('returns'/'describes'), so the action is inferred 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?

There is no explicit when-to-use, no exclusions, and no named alternative. Usage is strongly implied by the name and the content list (call it for shipping cost, payment option, or invoicing questions), which makes it usable but not guided.

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 cover read-only, idempotent, closed-world behavior, so the description adds items like the disclosure that matching quality depends on Czech terminology, and that results include price incl./excl. VAT, availability, delivery time and URL. That is real added context, but it omits ordering/relevance behavior, zero-result behavior and limit/cap 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?

Three dense sentences with the catalog identity and query-language hint front-loaded, followed by the return payload. Every sentence carries information, though the VAT repetition is mildly redundant with 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 five-parameter read tool with no output schema, the description helpfully summarizes what comes back (price, availability, delivery time, URL) and the annotations cover safety. Remaining gaps are pagination/limit behavior, result ordering and empty-result handling.

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 80%, so the schema already documents query, limit, in_stock_only and the price bounds, including the 'incl. VAT' phrasing used by the description. The description's contribution is the context that the catalog mixes own stock with partner warehouses, which motivates in_stock_only, but it gives no extra meaning to min/max price or limit.

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 verb and resource (full-text search in the puhy.cz catalog) and narrows scope to own stock plus partner warehouses. It implicitly separates itself from get_product and check_availability by being a search, but never names those siblings as alternatives.

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 through guidance on query formulation (Czech terms, part numbers, EAN, with examples in the schema) but never states when to use this versus get_product or check_availability, nor any exclusion. It is implied usage, not explicit routing.

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