check_availabilityPrueft, wie viele Einheiten eines Artikels aktuell bestellbar sind (unter Beruecksichtigung von Bestand und Lieferstatus).
| Name | Required | Description | Default |
|---|---|---|---|
| menge | No | Gewuenschte Menge (Default 1). | |
| artikel_id | Yes | Artikel-ID. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are present, so the description must disclose all behavioral traits. It only states the check is performed and mentions stock and delivery status, but fails to clarify if the operation is read-only, requires authentication, or has any 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, concise sentence that efficiently conveys the tool's purpose without redundancy. It is front-loaded with the action and uses clear language.
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 check tool with two parameters and no output schema, the description provides adequate but not complete information. It does not describe the return format or potential errors, which would be helpful for an agent.
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 parameter schema already covers both parameters with descriptions, so the tool description does not add significant meaning. It does not explain how 'menge' interacts with the availability check (e.g., whether it returns if the requested quantity is available). Baseline 3 applies due to 100% schema coverage.
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 checks how many units of an article are orderable, considering stock and delivery status. It uses a specific verb and resource, distinguishing it from sibling tools like get_product or get_variants, though not explicitly.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. No exclusions, prerequisites, or context are given, leaving the agent to infer usage from the action alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Each tool targets a distinct function: inventory checks, cart creation, product search, order management, loyalty, support. No overlap among search_products, list_products, and get_product; similarly, list_orders and request_address_change are unique.
All tools follow a verb_noun pattern (e.g., check_availability, create_cart, link_account) with consistent snake_case and English verbs, even though the shop is German. No mix of conventions.
19 tools is slightly above the typical 3-15 range for a focused server, but the number is justified by the breadth of shop functionality (product browsing, orders, cart, loyalty, guides, support). Not excessive.
Covers core shopping flows: product discovery, cart creation, order tracking, and account linking. However, missing tools for cart modification (add/remove items), order cancellation/returns, and checkout. These gaps may cause agent failures.