Gameshop Enter — retro Nintendo & Pokémon TCG (EU)

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

Annotations already indicate readOnly, idempotent, non-destructive, so the description doesn't need to rehash that. The description adds value by revealing the return includes a product-page URL and a direct checkout link, which is beyond a simple fetch and helpful for agents expecting only basic data.

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 function and scope, followed by additional return details. Every word earns its place; no fluff 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?

Given the tool is a simple single-parameter fetch, the description covers all essential aspects an agent needs to know: what it does, what it returns, and where the parameter comes from. The only minor gap is not explaining the format of the checkout link, but that's not critical for 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 already documents the 'sku' parameter with an example, achieving 100% coverage. The description adds context that the SKU comes from search_products, but that's minor; the schema is sufficient for parameter understanding.

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?

Clearly states it fetches price, stock, and details for a single product by SKU, and explicitly notes it is specific to 'Gameshop Enter', distinguishing it from generic alternatives. The verb 'Fetch' and resource 'one product' are specific and actionable.

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 it should be used when you have a SKU from search_products, as it says 'by its SKU (as returned by search_products)'. This suggests a clear workflow: search first, then fetch details. However, it doesn't explicitly say 'do not use for listing' or contrast with sibling tools like get_quote, so it's not a full 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, idempotent, and non-destructive behavior. The description adds useful behavioral context beyond that: the quote reflects the exact checkout shipping tier and any running promotion, quantity is expressed by repeating a SKU, and a checkoutUrl is included. It does not detail edge cases like invalid SKUs, but that is acceptable given the annotation coverage.

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 exact question it answers and then lists the output components in a compact, scannable sentence. Every clause earns its place; there is no filler or redundant restatement of the tool name or title.

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 two-parameter, read-only tool, it explains the main output fields, delivery scope, and quantity convention, which compensates for the missing output schema. A 5 would require more precision about the response shape or failure behavior, but the key information needed to call it correctly is present.

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 baseline applies. The description repeats the schema's 'repeat a SKU' guidance and adds the EU-country scope, but it does not meaningfully enrich parameter semantics beyond what the input schema already documents.

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 the exact user-facing question it answers and enumerates the output components: items, exact checkout shipping tier, running promotion, total in EUR, and checkoutUrl. This clearly separates it from siblings like get_product, search_products, and store_info, which do not provide all-in delivered pricing.

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 provides a clear trigger condition: use this when the user asks 'what does this cost in total, delivered?' and sets an explicit scope of the 27 EU countries. It does not explicitly name an alternative tool or state when not to use it, so it stops short of a 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 establish readOnlyHint, idempotentHint, and destructiveHint. The description adds valuable behavioral context: it searches a live catalog, tolerates language/typo variations, and discloses that checkoutUrl 'puts the item in the cart and opens checkout'—a non-obvious side effect worth flagging. Nothing 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 longer than average but each sentence carries scope, behavior, or return-value information. It is front-loaded with the core action and resource. Shipping destinations and 'personally tested' are slightly peripheral, but they do not obscure the essential invocation 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?

With no output schema, the description compensates by explicitly listing returned fields (price, stock status, url, checkoutUrl). Combined with full parameter schema coverage and safety annotations, the agent has enough context to invoke the tool correctly. Omitted details like result ordering or empty-result behavior are non-critical for a search tool.

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 parameters are already documented. The description enriches query semantics beyond the schema by explaining that abbreviations, Dutch/English names, typos, and localized Pokémon names are understood, and it gives concrete examples. This adds real meaning for how the query parameter behaves.

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 the live catalog of Gameshop Enter'. It enumerates the catalog scope (retro Nintendo games, consoles, PS4/5, Pokémon TCG) and explicitly states what the tool returns, clearly distinguishing it from sibling tools like get_product or store_info.

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 by emphasizing query flexibility (abbreviations, typos, multilingual names), but it never names alternatives or states when to choose search_products over get_product, get_quote, or store_info. The usage context is clear, but exclusions and alternative routing are left to inference.

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, idempotentHint, destructiveHint=false and openWorldHint=false, so the safety profile is fully covered without the description's help. The description adds that this is static store-level policy content (a useful hint that no external lookup is involved), but says nothing about data freshness, currency of shipping rates, or regional coverage beyond the EU.

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 front-loaded sentence that lists the highest-value items (shipping, returns) first with zero filler. It is a long run-on inventory rather than a grouped list, but every item earns its place and nothing is redundant.

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 parameters and no output schema, the description effectively doubles as the return-content specification by naming all the policy categories available. It covers what an agent needs to decide to call it, though it omits any note on granularity (e.g., per-country structure) or freshness of the rates.

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 the baseline of 4 applies. There are no arguments whose meaning the description would need to clarify, and the enumerated topics serve as an implicit map of what can be asked for.

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 enumerates the exact resource scope — shipping costs/delivery times per EU country, free-shipping thresholds, return policy, payment methods, languages, verification profiles — which clearly separates it from the product- and quote-oriented siblings. It lacks an explicit verb ('returns'/'retrieves'), but the noun-phrase inventory leaves no ambiguity about what the tool surfaces.

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 statement or reference to alternatives such as get_quote. Usage is only implied by the enumerated topics: an agent can infer it should call this for policy/shipping/returns questions rather than product lookups, but that inference is left entirely to the reader.

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