Blumen Komander München

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 adds useful behavioral context: delivery is always free and a same-day cutoff is returned. However, it does not disclose whether the operation is read-only, what happens when delivery is unavailable, or any error conditions, leaving some transparency gaps.

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 (three short sentences) and each sentence adds value: the core purpose, the free-delivery fact, and the return value. There is no filler or 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?

With no output schema, the description does mention the returned same-day cutoff, which helps. However, it does not fully describe the response shape or how availability is indicated (e.g., boolean, message). Given the simplicity of the tool, this is a minor gap and the description is otherwise complete enough for a basic check.

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% for the two parameters (date and postalCode). The description restates these concepts but adds no new parameter-level meaning beyond what the schema already provides, so a 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 uses a specific verb 'prüft' (checks) with a clear resource (delivery capability of Blumen Komander München) and scoping parameters (date and postal code). It also mentions the return of a same-day cutoff, making it distinctly different from sibling tools like create_cart or get_order_status.

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 makes it clear this is a pre-order availability check, but it does not explicitly name when not to use it or mention alternative tools. No exclusions are stated, and the context is clear enough for basic 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 provided, the description carries the full burden of behavioral disclosure. It reveals the conditional billing logic (if billing address differs, set billing_* fields; if not, use delivery address) and enumerates payment options with their corresponding provider values. It also discloses the order of actions (ask questions, then create cart). It does not mention side effects or error behavior, but the core behaviors are well covered.

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 moderately sized but well-structured. Each sentence adds value: purpose, fields set, billing logic, and prerequisite questions. The use of numbered questions improves readability. While slightly lengthy, it avoids redundancy and includes only relevant operational details.

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 complex tool with 17 parameters, no output schema, and no annotations, the description provides comprehensive contextual guidance: it explains the core action, conditional billing behavior, and the necessary customer questions. The only gap is the lack of information about the return value or next steps after cart creation, but this is a minor omission given the absence of an output schema.

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?

Although the input schema already describes all 17 parameters with 100% coverage, the description adds meaningful context beyond the schema. It explains the conditional relationship between billing and shipping addresses, and it maps payment provider enum values to user-facing options (Kreditkarte/Apple Pay/Google Pay to stripe, etc.). This semantic enrichment helps the agent correctly populate parameters.

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's purpose: 'Legt einen Warenkorb bei Blumen Komander an und fügt einen Blumenstrauß hinzu' (creates a cart and adds a bouquet). The verb 'Legt an' is specific and resource-oriented, and it lists the fields set. It is distinct from sibling tools like check_availability, get_checkout_link, and search_flowers, which serve different functions.

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 explicit usage context by instructing the AI to ask two prerequisite questions before creating the cart (billing address match, payment method preference). It does not explicitly mention when not to use this tool or alternatives, but the sibling tool list makes it clear this is the only cart-creation tool. The prerequisite questions serve as clear when-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 provided, the description carries the full burden of behavioral disclosure. It explains the main outcome (the customer clicks the link and pays) but does not mention potential side effects, error cases (e.g., invalid cart ID), link expiration, or authentication requirements. This is adequate but leaves some behavioral unknowns.

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 short, clear sentences that immediately state the function and the user-facing outcome. No wasted words and well 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 the tool's simplicity (one parameter, no output schema), the description covers the essential purpose and context. It could be more explicit about the return format (e.g., JSON containing the URL), but the phrase 'returns the direct checkout link' sufficiently implies the output for most use 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?

The input schema already fully describes the single parameter 'cart_id' with 100% coverage. The description adds only that the cart must be existing, which is arguably already implied by the schema description and tool purpose. Thus, minimal added value beyond the structured 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 returns a direct checkout link for an existing shopping cart, using a specific verb ('returns') and resource ('checkout link for an existing cart'). It distinguishes itself from sibling tools by focusing on the payment link rather than order status or 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?

The description provides clear context: the tool is for existing carts, implying the user must have a cart ID ready. It does not explicitly mention alternative tools or when not to use it, but the purpose is sufficiently distinct that the intended usage is clear.

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. It discloses that verification via order number and email is required and explicitly states that no address data is returned, which is a helpful privacy-related transparency. It does not mention side effects or error behavior, but for a simple status lookup it provides useful behavioral context beyond the basic function.

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 purpose, and every clause adds value (status range, delivery date, verification requirements, explicit exclusion of address data). No redundant or filler content.

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 low-complexity tool with full schema coverage and no output schema, the description is complete. It explains what the tool returns (status and delivery date) and what it does not return (address data), and the verification requirements make the usage context self-contained.

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 provides complete descriptions for both parameters (email and order_number), covering 100% of parameters. The description repeats essentially the same information ('Bestellnummer aus der Bestellbestätigung' and 'E-Mail-Adresse des Bestellers'), adding no new semantic meaning beyond what the schema already conveys.

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 queries the status of an existing order at Blumen Komander, enumerating the possible statuses (received, in preparation, on the way, delivered) and including the delivery date. It uses a specific verb ('Fragt ab') and resource ('Status einer bestehenden Bestellung'), and it is clearly distinct from sibling tools like check_availability or get_shop_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 when to use this tool (when checking order status) and even states the required verification data. It does not explicitly name alternatives or exclusions, but the purpose is so distinct from siblings that the context is clear 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?

With no annotations, the description carries full behavioral disclosure. It states the tool returns information, which is a read-only operation, and lists the specific categories returned. It does not mention side effects or errors, but none are expected for such a simple info tool.

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 directly lists the returned information categories with no wasted words. It is 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 simplicity of the tool (no parameters, no output schema, no annotations), the description is complete. It enumerates the return content sufficiently for an agent to understand what the tool provides.

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 description does not need to explain parameter semantics. The baseline for zero parameters is 4, and the description appropriately adds meaning by detailing what information is returned.

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 explicitly states the tool returns general information about Blumen Komander, listing categories (opening hours, delivery area, contact, payment methods). This clearly distinguishes it from sibling tools like search_flowers or check_availability, which have different purposes.

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 does not explicitly state when to use this tool versus alternatives, but the nature of the description (general shop info) implies usage. No exclusions or alternative recommendations are provided, so it is not explicit 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 provided, the description carries the burden of behavioral disclosure. It adds that the tool searches 'verfügbare' (available) bouquets and filters by occasion, color, style, and budget, which are behavioral traits. However, it does not state whether it is read-only, any rate limits, or what the result contains, leaving some gaps.

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: one clause for the search action and one for the filtering capabilities. It is front-loaded with the core purpose and contains no unnecessary words or repetition. 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?

There is no output schema, so the description should explain what the tool returns (e.g., list of bouquets, matching criteria, pricing) but it does not. It also does not mention pagination, sorting, or how results relate to other tools like check_availability or create_cart. For a tool with 6 optional parameters and no return specification, the description feels incomplete.

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 100% description coverage, so the baseline is 3. The description groups parameters into categories ('Anlass, Farbe, Stil, Budget') and adds 'Budget' as a conceptual umbrella for minPrice/maxPrice, but it omits 'flower_type' which exists in the schema. Overall, it adds minor semantic grouping but does not significantly extend the schema's existing parameter details.

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's purpose: 'Sucht verfügbare Blumensträuße bei Blumen Komander München' (searches for available flower bouquets at Blumen Komander Munich). It uses a specific verb ('Sucht') and resource ('verfügbare Blumensträuße'), and the filtering attributes distinguish it from sibling tools like check_availability, create_cart, or get_shop_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: it is the tool for searching/filtering bouquets, but it does not explicitly say when to use it over siblings or when not to. No alternatives or exclusions are mentioned. The purpose alone gives some guidance, but not explicit criteria for selection.

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