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

With no annotations, the description carries full responsibility for behavioral disclosure. It mentions the audit requirement ('reason... required for audit') and the priority options, but does not disclose side effects such as whether calling it multiple times creates duplicate orders, or if special permissions are needed. This is a moderate disclosure, not comprehensive.

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 well-organized with a purpose sentence, usage guideline, and an Args list. The first two sentences are slightly redundant ('delta >= 6' vs 'units_sold − available is at least 6'), but otherwise every sentence contributes important information. This is efficient and easy to scan.

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 6-parameter create tool with an output schema, the description covers all parameter semantics, the trigger condition, and even a default value. The only missing piece is a note about what happens on success (e.g., returns order ID), but the output schema covers return values, so the description is effectively complete for an agent to invoke it 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 has zero description coverage, so the description must explain all parameters. It does this thoroughly: each parameter has an example, constraints (e.g., quantity > 0), and clarification that order_reason is optional, while priority lists its enum values and default. This fully compensates for the absent 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 opens with 'Raise a replenishment order' – a specific verb + resource that clearly identifies the tool's action. It further distinguishes itself from siblings by tying to 'get_on_hand_vs_pos delta >= 6', making its purpose unique and unambiguous.

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 explicit guidance: 'Call after get_on_hand_vs_pos when units_sold − available is at least 6' and 'Prefer ordering in multiples of product.case_pack from that response.' This provides a clear trigger condition and ordering preference, but it does not explicitly mention alternatives or when not to use the tool, so it falls just 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?

With no annotations provided, the description carries the full burden of disclosing behavioral traits. It explains the computation of delta (units_sold − available), the default time window, and what data is returned. It stops short of explicitly stating 'read-only' or listing side effects, but the 'get' verb and return-focused wording strongly imply a safe, non-mutating operation.

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 efficient: a single paragraph with line breaks for readability, front-loaded with the core purpose, and every sentence adds value. It includes examples, return details, and an action rule without any filler.

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 that an output schema exists, the description still adds value by explaining what the outputs represent (delta, product block) and how they support order sizing. It also covers all four parameters thoroughly and includes actionable guidance. This is complete for the tool's complexity.

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 0%, so the description must fully compensate. It does: store_id and sku get concrete examples, sku is tied to a product, reason is explained as required for audit, and since has its default behavior fully documented. Every parameter is meaningfully described beyond the raw 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's purpose: to get current on-hand inventory and POS sales for a SKU at a store. It specifies the output components (inventory, POS, delta, product block) and clearly distinguishes itself from sibling tools like list_stores and create_replenishment by focusing on the inventory/sales view rather than store catalog or replenishment actions.

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 guidance on when to use this tool and when to escalate: if delta >= 6, call create_replenishment. It also explains the default POS window (last 24h) and how to override it with 'since', giving clear usage context for the category buyer view.

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 are absent, so the description carries the burden. It implies a read-only operation via 'Get' and adds the context that 'reason' is required for audit purposes. However, it does not explicitly state side effects, auth requirements, error behavior, or any other behavioral traits.

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 extremely concise: a one-sentence purpose followed by a structured Args list. Every sentence carries relevant information, and the format is front-loaded with the main 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?

With an output schema present, return values are covered. The description adequately explains the tool's purpose and parameters, but lacks context on when to use it relative to siblings and provides minimal behavioral detail. Overall, it is functional but not fully complete for a tool with sibling alternatives.

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 0%, so the description must define parameters. It provides brief but meaningful definitions for each parameter, and the 'reason' parameter is further contextualized as an audit requirement. However, the definitions are minimal and mostly echo the parameter names, offering limited additional semantic depth.

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 a specific verb ('Get') and resource ('status of a replenishment order'), and identifies the key identifying parameters (store_id, order_id). It is distinct from sibling tools by focusing on order status, but it does not explicitly differentiate itself from alternatives such as get_store or create_replenishment.

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. There is no mention of context, exclusions, or why this tool might be preferred over a sibling like get_store or get_on_hand_vs_pos.

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 must carry the transparency burden. It usefully discloses that the 'reason' argument is required for audit, and gives format examples for store_id. However, it does not explicitly state whether the operation is read-only, whether special permissions are needed, or any limitations on returned details, relying on the output schema to clarify output.

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: one sentence states the core purpose, followed by a clean args list. Every sentence adds value with no redundancy or filler.

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 simple get-by-id operation, an output schema is present, and the description includes the audit requirement, the tool is well-specified. It could have strengthened contextual guidance by explicitly referencing list_stores for discovering store_ids, but this is a minor omission for a low-complexity 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 schema has no descriptions (0% coverage), so the description provides essential meaning for both parameters. It clarifies 'store_id' with an example format and explains 'reason' as an audit requirement, going well beyond the bare schema field names.

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 ('Get details'), the resource ('a single store'), and the required identifier ('store_id'). The qualifier 'single' distinguishes it from sibling tool list_stores, making the purpose unambiguous.

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 you have a known store_id and need just that store's details, contrasting with list_stores. It does not explicitly mention alternatives or exclusions, but the by-id framing provides clear context for when this tool should be selected.

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 must disclose behavioral traits itself. It mentions the reason parameter is 'required for audit', which implies logging side effects, but it doesn't describe authentication needs, rate limits, or whether the operation is strictly read-only. 'List' implies a safe read, but more detail would improve 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 extremely concise and front-loaded with the core purpose. The 'Args' line adds necessary parameter info without any fluff. 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?

The tool is simple (one parameter, no nested objects) and has an output schema, so the description doesn't need to detail return values. It provides the required parameter's purpose, the tool's scope, and the key fields. This is sufficient 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 0%, so the description must compensate. It fully explains the only parameter: 'reason: Why you are listing stores (required for audit).' This adds meaning beyond the schema, clarifying both purpose and requiredness.

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 starts with a clear verb and resource: 'List all StoreLink stores' and enumerates the fields returned (id, name, city, country code, active flag). This distinguishes it from sibling tools like get_store, which targets a single store, and the replenishment 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 implies this is for listing all stores, but it does not explicitly state when to choose this over alternatives or mention any exclusions. The 'all' qualifier suggests bulk retrieval, but there is no direct guidance on when to use this vs. get_store or the replenishment tools.

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