shop

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

The description adds context beyond the schema by mentioning stock and delivery status consideration. It implies a read operation and does not contradict any annotations (none provided). However, it does not disclose response format or error behavior.

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?

One single sentence that is front-loaded with the key action and resource. No unnecessary words; every part earns its place.

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 no output schema, the description implies the return value (a quantity) but does not explicitly state it. It is adequate for a simple tool but could be more complete by mentioning response structure or edge cases.

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% with both parameters described. The description adds little beyond the schema, only implying the output is a quantity. Baseline score 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 states the verb 'prueft' (checks) and the resource 'how many units of an article are currently orderable', distinguishing it from sibling tools like get_product or get_variants.

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 checking orderable quantity but does not explicitly state when to use versus alternatives or when not to use it. There are no exclusions or recommendations provided.

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?

No annotations are provided, so the description must fully convey behavioral traits. It only states that the inquiry is forwarded as a ticket and requires a valid email. It omits details like response time, whether the operation is synchronous, or any side effects. This is insufficient for a tool with no annotation safety net.

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 two sentences, front-loaded with the core action, and contains zero superfluous information. Every sentence earns its place.

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 simplicity (submitting a support request) and lack of output schema, the description is adequate but minimal. It covers purpose and a key requirement but misses behavioral context (e.g., whether a confirmation is provided, ticket ID). An agent could use the tool correctly with this information, but additional context would improve reliability.

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% (all four parameters have descriptions in the schema). The description adds no additional semantic info beyond what the schema already provides. 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 clearly states the verb and resource: 'Leitet eine Kundenanfrage als Ticket an das Team weiter' (forwards a customer inquiry as a ticket to the team). This distinguishes the tool from all siblings, none of which suggest a similar support ticketing function.

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 mentions a requirement ('Erfordert eine gueltige E-Mail fuer die Rueckmeldung') but provides no explicit guidance on when to use this tool versus alternatives or when not to use it. Usage is implied for contacting support, but lacking exclusions or comparison to other tools.

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 effectively discloses key behavioral traits: it creates a cart, returns a checkout URL, and does not complete the order. This is valuable transparency. However, it does not mention side effects (e.g., whether the cart is saved to an account) or permissions needed.

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 concise sentences: the first states the action and return, the second provides a critical warning. Every sentence earns its place, and the key information 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 no output schema, the description adequately states the return value (a checkout URL). The tool is simple with one parameter, so the description is largely complete. However, it could be more explicit about the structure of the checkout URL or any additional response fields.

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 schema already describes parameters. The description adds no additional meaning beyond what is in the schema; it only mentions that the cart is 'prepared' and returns a checkout URL. 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 clearly states the action: creating a prepared cart and returning a checkout URL. It explicitly notes that the tool does NOT complete an order, distinguishing it from order placement. The resource (cart) and verb (anlegen) are specific and differentiate from sibling tools like list_orders.

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 provides clear context: it only creates a cart and returns a checkout URL, and importantly, the order must be completed manually by the customer. It implies when to use this tool (for initiating checkout) and when not (if you want to finalize an order). However, it does not explicitly name alternative tools among siblings.

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?

No annotations are provided, but the description discloses that the output is plain text intended for AI advisory purposes. It is a read-only operation with no destructive side effects. The description adds useful context beyond the schema fields.

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 sentence that front-loads the purpose and includes all essential information. No redundant words.

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 retrieval tool with optional parameters and no output schema, the description is complete. It explains what is returned (full text in plain text) and how to specify the article. The sibling search_guides provides necessary context for finding the identifier.

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 both parameters having clear descriptions. The tool description adds no additional parameter semantics beyond stating that id or url can be used. 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 clearly states the tool returns the full text of a guide article (verb 'liefert', resource 'Ratgeber-Artikel'), specifying identification by ID or URL slug. This distinguishes it from siblings like search_guides which only search for articles.

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 when the full article text is needed, likely after using search_guides to find the article. It does not explicitly state when not to use it, but the context and sibling list provide sufficient 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?

No annotations are provided, so the description carries full burden. It mentions the auth requirement (token from link_account) but does not disclose whether the operation is read-only or if it has any side effects. The description lacks explicit behavioral traits like data mutability or rate limits.

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-load the purpose and essential requirement. No redundant information; every word earns its place.

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 retrieval tool with one parameter and no output schema, the description adequately states returned data (points, level, rewards) and a prerequisite. Could mention read-only nature, but overall sufficient.

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% (only 'token' parameter with description). The description reinforces that the token must be linked via 'link_account', but adds limited new meaning 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 clearly states the tool retrieves loyalty points balance, level, and redeemable rewards for a linked customer, specifying the verb (get), resource (loyalty info), and scope (linked customer). It distinguishes itself from sibling tools, none of which focus on loyalty.

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 requires a token linked via 'link_account', providing a clear prerequisite for use. However, it does not explicitly state when to use this tool versus alternatives, though no alternative loyalty tool exists among siblings.

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?

No annotations are provided, so the description carries the full burden. It indicates a read operation ('informationen') but does not explicitly state it is non-destructive, idempotent, or safe. There is no mention of side effects or requirements, leaving the agent to infer behavior.

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, focused sentence that wastes no words. It is front-loaded with the key information and immediately conveys the tool's purpose.

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 simplicity of the tool (no parameters, no annotations, no output schema), the description is adequate. It tells the agent what information to expect (pickup details and next available dates). For a straightforward info retrieval tool, this is sufficient.

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 has zero parameters, and the input schema already reflects this clearly. The description does not need to add parameter semantics because none exist. Baseline for 0 parameters is 4.

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 tool provides information about pickup at the factory outlet including next available dates. The verb 'get' is implicit, and the resource 'pickup info' is well-defined. It distinguishes from siblings like check_availability or get_shop_info by focusing on pickup logistics.

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?

No guidance is provided on when to use this tool versus alternatives. The description does not specify when not to use it or mention any prerequisites or context. For example, it does not clarify if this is for order pickup or general outlet info.

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?

No annotations provided, so description carries full burden. It lists outputs but does not mention read-only nature, authentication, side effects, or limitations like pagination. Minimal behavioral context.

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?

Single sentence front-loads purpose and lists key outputs without waste. Highly 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?

Adequate for a simple tool given no output schema; covers returned fields. Lacks usage guidelines and behavioral details, leaving gaps.

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% with descriptions for both parameters. Description adds context that parameters are IDs/SKUs but does not provide additional semantics beyond schema, baseline 3.

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?

Clear verb+resource: retrieves a single product by ID or SKU, listing returned fields. This distinguishes it from sibling tools like search_products (multiple) and get_variants (variants).

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?

Implied usage (when you have an ID or SKU) but no explicit guidance on when to use versus alternatives, no exclusions or when-not-to-use.

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 discloses the behavioral traits of returning similar products filtered by manufacturer and top-seller status. However, it lacks details on response format, pagination, or limits. Since no annotations exist, the description carries the full burden, and it provides moderate transparency.

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, concise sentence that efficiently conveys the tool's purpose and constraints. No unnecessary words, front-loaded with key 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?

Given the low complexity (one parameter, no output schema), the description is adequate but leaves gaps about the return structure (e.g., number of items, ordering). For an agent to fully leverage the tool, more detail on output would help, but the core functionality is 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?

The description adds context to the parameter 'artikel_id' by calling it an 'anchor' article ID, implying it is the reference point for recommendations. The schema already describes it as 'Anker-Artikel-ID.', so the description reinforces and adds slight value beyond 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 clearly states the tool provides similar, deliverable products based on an anchor article, with criteria of same manufacturer and top-sellers. This distinguishes it from siblings like list_bestsellers (general top-sellers) and search_products (general search).

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 use when recommendations for a specific product are needed, but does not explicitly state when to use this tool versus alternatives like get_product or list_bestsellers. No when-not or exclusion criteria are provided.

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?

No annotations are provided, so the description carries full burden. It discloses that it returns data from special pages and the default behavior without 'thema', but lacks information on side effects, authentication requirements, rate limits, or response format, leaving significant gaps for a complete behavioral understanding.

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 efficiently conveys the tool's purpose and the conditional behavior. Every word serves a purpose, with no 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?

While the tool is simple (one optional parameter, no output schema), the description does not specify the structure of the returned data (text, URLs, etc.) or any limitations. It provides the essential purpose and default behavior but leaves some ambiguity about the output format.

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% with parameter description listing examples. The description adds the key insight that omitting 'thema' returns a list of pages, which is not in the schema. This adds meaningful context beyond the schema's examples.

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 it returns public shop/contact data from special pages like impressum or AGB, and specifies that without the 'thema' parameter it returns a page list. This gives a specific verb-resource mapping but does not explicitly distinguish it from 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 does not provide any guidance on when to use this tool versus alternatives, nor does it mention when not to use it. It only implies usage for retrieving public shop data without comparative context.

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 provided, the description carries the full burden of behavioral disclosure. It only describes the output content (variants with packaging and conversion factor), but does not mention any behavioral traits such as read-only nature, required permissions, rate limits, or potential side effects.

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 sentence of 13 words, which is concise and front-loaded with the main purpose. However, it could benefit from a brief note on usage context. For a simple tool, it is appropriately sized.

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 has one parameter and no output schema, the description provides the essential purpose and return elements. However, it lacks behavioral context (e.g., read-only nature) and does not explain the output format or how the conversion factor is structured. It is minimally adequate but not thorough.

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 input schema already describes the single parameter 'artikel_id' as 'Artikel-ID eines Produkts der Gruppe.' (100% coverage). The description adds minimal extra meaning (tying it to 'Versionsgruppe'), but does not clarify the expected format or constraints beyond 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 clearly states that the tool retrieves all variants of an article's version group, including packaging and conversion factor. It uses specific terms ('Varianten', 'Gebinde', 'Umrechnungsfaktor') and a concrete example. However, it does not explicitly differentiate from sibling tools like 'get_product' or 'search_products', and the term 'Versionsgruppe' might be ambiguous.

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?

No guidance is provided on when to use this tool versus alternatives like 'get_product' or 'search_products'. The description only states what the tool does, leaving the agent to infer usage context.

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?

No annotations are provided, so the description carries the full burden. It transparently describes the output (login URL and token) and the required subsequent action (login in browser). However, it omits details like token expiration, idempotency, or potential side effects, which would make it fully 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?

The description is a single sentence that efficiently conveys the action, outputs, and post-usage instructions. It is well-structured and front-loaded with the essential purpose.

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 no parameters and no output schema, the description fully covers the tool's behavior: what it does, what it returns, and how to proceed. It also references sibling tools to provide context.

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 input schema has zero parameters and 100% schema coverage, so the baseline is 3. The description adds no parameter information because there are none.

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 tool initiates account linking and produces a login URL and session token. It distinguishes itself from sibling tools by specifying which tools can be used after the linking process.

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 when to use the tool (to start account linking) and provides clear follow-up instructions: after login, use the token with specific tools (list_orders, get_loyalty, request_address_change). It effectively guides the agent on the workflow.

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?

No annotations provided, so description bears full burden. It discloses the time window (last 90 days) and availability filter, but lacks details on pagination, sorting order, or return format. Basic transparency is present.

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 concise sentence in German that packs essential information: what (bestsellers), scope (available, last 90 days), optional filter, and use case. Zero wasted words.

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, description should ideally detail return fields. It states items are best-selling and available but omits attributes, pagination, or ordering. Adequate for a simple list with sibling tool context.

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% with both parameters documented. Description adds the time window context and reinforces category optionality, but does not exceed the schema's value significantly. 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 states the tool lists best-selling available items from the last 90 days, with optional category filtering. It distinguishes from sibling tools like list_products or list_offers by focusing on bestsellers and time window.

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 recommends the tool for 'recommendations and entry questions', providing clear usage context. It does not specify alternatives or when not to use, but the guidance is helpful enough.

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?

No annotations provided, so description carries full burden. It discloses return fields (Name, ID, URL, Parent) but does not mention pagination, ordering, or any side effects. Basic transparency but not thorough.

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 efficiently conveys the tool's purpose and output. No wasted words.

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 parameterless list tool without output schema, the description is adequate. It explains what is returned and its purpose. Could mention ordering or filtering if applicable, but not necessary.

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?

No parameters exist, and schema coverage is 100%. The description adds value by specifying the returned attributes, which is beyond the empty 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 clearly states it lists all shop categories with specific attributes (name, ID, URL, parent) for navigation. It distinguishes from sibling tools like list_products or list_offers by specifying the resource type.

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?

No explicit guidance on when to use this tool versus alternatives like list_bestsellers or list_offers. It implies use for navigation but lacks when-not-to-use context.

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?

No annotations are provided, so the description carries full burden. It discloses that prices are already offer prices and sorting is by popularity, but does not mention whether this is a read-only operation, authentication needs, pagination, or any side effects. Adequate but incomplete.

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 short sentences, front-loaded with the core purpose, no wasted words. Efficient for quick scanning.

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?

No output schema exists, but the description does not explain the return structure (e.g., fields like product ID, name, price). Also lacks guidance on pagination or how to handle multiple results. Adequate for a simple list but could be more complete.

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% with the limit parameter described (max 30, min 1, default 12). The description adds no additional meaning beyond the schema, so 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 clearly states the tool lists currently reduced items with active special prices, sorted by popularity. This distinguishes it from siblings like list_bestsellers (bestsellers not necessarily discounted) and search_products (all products). The verb 'list' and resource 'offers' are specific.

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 use for finding current discounts but does not explicitly state when to use this tool over alternatives (e.g., list_bestsellers, search_products), nor does it mention when not to use it. The context is clear but not explicit.

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 covers behavioral traits: it requires authentication (token from link_account) and lists the data it returns. It could mention pagination or read-only nature.

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 efficient sentences with no wasted words.

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?

Adequate for a list tool, but missing output schema means response format details are lacking. The description mentions included elements but not structure.

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 baseline 3. The description does not add extra meaning beyond schema 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 it lists orders of the linked customer, including status, payment status, and delivery/tracking. This distinguishes it from other tools like search_products 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?

It explicitly requires a token from link_account, indicating a prerequisite. However, it does not provide when-not-to-use or contrast with sibling tools.

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?

No annotations provided, so description must cover behavioral traits. It mentions pagination and optional filters but does not disclose response format, error handling, or whether it is read-only (implied but not stated).

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?

Single sentence, front-loaded with the core purpose, no wasted words. Efficient and clear.

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 and many sibling tools, the description covers the basics but could be more complete to fully differentiate. Lacks details on ordering, response structure, or edge cases.

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 baseline is 3. The description adds value by summarizing optional filters, but does not provide additional meaning beyond what the schema already states.

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?

Description clearly states it returns a paginated product list with optional filters by category or brand. It distinguishes from siblings like 'get_product' (single product) and 'search_products' (search functionality).

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?

No explicit guidance on when to use this tool versus alternatives (e.g., search_products for text search, get_product for a single item). The description does not provide context or exclusions.

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?

No annotations are provided, so the description carries the full burden. It indicates this is a request (ticket to team) implying asynchronous behavior, and mentions prerequisites. However, it does not disclose whether the operation is idempotent, what effects occur, or any error scenarios.

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 essential information, no filler. Action verb is first. Every sentence contributes value.

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 description lacks information about return value or outcome (e.g., ticket ID, success confirmation). With no output schema, the description should explain what the agent can expect, but it only mentions 'ticket to team'. Error handling and side effects are also omitted.

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% with each parameter described. The description adds minimal additional meaning beyond the schema; it repeats that 'auftrag' is optional but does not clarify format or constraints. 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 clearly states the action: 'requests a delivery address change' for 'a not yet shipped order of the linked customer'. It specifies the resource (order) and the condition (not yet shipped), distinguishing it from siblings like list_orders or link_account.

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 notes prerequisite ('requires a token linked via link_account') and context ('for a not yet shipped order'). It doesn't provide explicit when-not-to-use or alternatives, but the context is clear enough for most agents.

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?

No annotations are provided, so the description must cover behavioral traits. It discloses that without a query it returns newest posts, which is useful. However, it does not mention authentication, rate limits, or whether the operation is read-only (though search implies read-only). The description is adequate but not fully 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?

The description is two sentences, front-loading the purpose and following with a usage hint. Every sentence is necessary and there is no fluff. Highly concise and well-structured.

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 has 2 parameters, no output schema, and no annotations, the description covers the core functionality, use case, and behavior without query. It lacks details on return format but that is acceptable without an output schema. It is complete enough for an agent to use 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 coverage is 100% with both parameters described. The description adds value beyond the schema by explaining that query is optional and defaults to returning newest posts. This behavioral nuance is not in the schema, making the description helpful.

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 tool searches the 'Tabak-Brucker Ratgeber/Blog' covering guides, buying advice, care, and knowledge. It uses specific verbs ('durchsucht') and identifies the resource, distinguishing it from sibling tools like search_products (product search) and get_guide (specific guide retrieval).

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 a clear use case: 'Ideal, um Kundenfragen mit den Inhalten des Shops zu beantworten.' It also notes behavior without query ('neueste Beitraege'). However, it does not explicitly mention when not to use it or compare to alternatives, though the sibling context implies differentiation.

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?

No annotations are provided, so the description carries the full burden. It states it returns matching products with price and availability, indicating a read operation. However, it lacks disclosure of any side effects, authentication requirements, rate limits, or behavior on empty results. The description is minimal but not misleading.

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 two sentences, front-loaded with the main action, and contains no redundant information. Every word contributes meaning.

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 no output schema, the description sufficiently explains return values (price and availability). Parameter details are covered in schema. Missing pagination or offset guidance, but for a simple search tool with limit, it is adequate.

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% with both parameters described. The description adds context that the search covers name, article number, and EAN fields, which goes beyond the schema. However, it does not explain the limit parameter beyond what is in the schema. Baseline for high coverage is 3; the description adds marginal value.

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 tool performs a full-text search across product catalog fields (name, article number, EAN) and returns products with price and availability. This distinguishes it from sibling tools like list_products (likely all products) and get_product (single 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 does not explicitly guide when to use this tool vs alternatives. It implies usage via its name and description (search vs list), but no when-not-to-use or comparison with similar siblings like search_guides or get_recommendations.

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