Gameshop Enter — retro Nintendo & Pokémon TCG (EU)

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

No annotations are provided, so the description carries the behavioral burden. It communicates that this is a non-mutating fetch that returns current price, stock, and product details, and it adds the useful detail that the SKU must come from search_products. It does not cover error behavior or exact response structure, but for a simple read operation it is reasonably transparent.

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 with no filler. The most important information—what fields are fetched and how to obtain the SKU—is front-loaded, and the sibling reference is woven in without extra verbosity.

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 one-parameter, read-only lookup tool, the description covers the input source, the output content, and the product-page URL. There is no output schema, but the listed return fields are sufficient for an agent to know what to expect. Minor gaps like error behavior are not material to invoking this 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 the single 'sku' parameter with 100% coverage and an example. The description adds meaningful provenance by stating the SKU is 'as returned by search_products', which helps the agent know how to obtain a valid value, going beyond the bare 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 states a clear verb ('Fetch'), a specific resource ('one Gameshop Enter product'), and the key identifier ('by its SKU'). It also differentiates itself from search_products by explicitly sourcing the SKU from that sibling tool, so an agent can tell the two apart.

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 clear context: use this tool when you already have a product SKU, and obtain that SKU via search_products. It does not explicitly enumerate alternatives or exclusions beyond search_products, but the usage context is unambiguous enough for correct selection.

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?

With no annotations, the description bears the full burden and does well: it discloses output composition, the quantity-by-repeating-SKU convention, and the 27-EU-country limitation. It does not cover error cases or quote validity, which keeps it from 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?

Three sentences, with the purpose front-loaded and each sentence adding distinct value: expected outputs, quantity handling, and checkoutUrl. No filler or redundant restatement.

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?

Even with no output schema and no annotations, the description covers what the agent will receive, how to express quantity, and the geographic scope. Minor gaps remain around non-EU or invalid inputs and whether checkoutUrl expires, but they are not critical 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?

Schema description coverage is 100%, so the baseline is 3. The description adds little beyond the schema: the quantity convention is already in the skus parameter description, and the '27 EU countries' phrasing slightly enriches the country parameter scope.

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 uses a specific verb ('answer what does this cost in total?') and identifies the resource: a delivered price quote with items, shipping tier, promotion, total EUR, and checkoutUrl. The EU-only scope further distinguishes it from siblings like get_product and 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 opening phrase is a clear trigger: use this tool when the agent needs a total delivered cost. It gives unambiguous context, though it does not explicitly name alternatives or state when-not-to-use it beyond the EU scope.

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?

With no annotations, the description carries the full burden, and it explains real behavior: the catalog is live, products are personally tested, shipping is limited to the EU, and the query matcher tolerates abbreviations, Dutch/English names, and typos. It also discloses the return contents (EUR price, stock status, buy URL), which meaningfully informs an agent beyond the schema.

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 primary action and resource. Some details like 'personally tested' and 'shipping to all 27 EU countries' are contextual rather than essential to invoking the tool, but the text remains efficient and focused.

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 search tool with a well-covered schema and no output schema, the description provides enough return-value details (price, stock, buy URL) and query-handling behavior to guide correct use. It doesn't specify result ordering or pagination, but these are not critical for basic invocation and are partially covered by the schema's limit parameter.

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 the baseline is 3, but the description adds genuine value for the query parameter by explaining it understands abbreviations, bilingual names, and typos. This is behavior the raw schema doesn't capture; other parameters like limit and maxPrice are already clearly described in 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 opens with 'Search the live catalog of Gameshop Enter', a specific verb-resource pair that identifies the tool's job. It distinguishes search from the siblings get_product, get_quote, and store_info by emphasizing catalog-wide search and product listing returns.

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 this is the tool for searching the catalog by title, character, franchise, or SKU, and notes it accepts abbreviations and typos. However, it does not explicitly state when to prefer this over get_product or provide any when-not-to-use 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?

With no annotations, the description carries the full burden of behavioral disclosure. It lists the information content but does not explicitly state that the tool is read-only, returns static reference data, or explain any limitations or freshness caveats. It is not misleading, but it adds limited behavioral context beyond the 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 a single, well-structured sentence that front-loads the most important content (shipping costs and delivery times) and then efficiently lists the remaining topics. Every phrase adds substantive 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 no-parameter, no-output-schema information tool, the description covers the main content categories an agent needs to decide whether to invoke it. It does not explicitly describe the response format, but that is a minor gap for a static store-info 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?

The tool takes zero parameters, so the schema fully covers invocation needs. The description adds semantic context about the kind of information returned, which is useful for selection even though no parameter explanations are required.

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's purpose: it provides store policy information including shipping costs, delivery times, free-shipping thresholds, returns, payments, and languages. This is a specific, concrete resource and is easily distinguishable from the sibling tools focused on products, quotes, and searches.

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 when to use this tool: when the agent needs store-level policies or reference information. However, it does not explicitly state alternatives or when not to use it, such as clarifying that it is not for product-specific queries.

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