Pinoyshki Bakery

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint:false, so the description need not restate safety. It adds valuable behavioral context: the exact rules applied (closed Mondays, same-day and catering constraints), the return of problems and available times, and the explicit directive to never promise a date/time the result did not allow. This goes beyond annotations and helps the agent set expectations and act on results correctly.

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 dense but highly structured: it leads with the core purpose and output, then lists the applied rules in a compact block, and closes with actionable guidance for customer interaction. Every sentence adds necessary information for correct tool usage—no filler. The logical flow (purpose → rules → usage instruction) makes it easy to parse even for a complex tool.

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 complexity (8 parameters, intricate business rules), the description is remarkably complete. It specifies what rules are enforced, what the result contains, and how to use the result (show lines/subtotal, never promise disallowed times). With an output schema present, the description does not need to detail return formats. It covers all necessary guidance for an agent to invoke it correctly and interpret the outcome, leaving no critical 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?

Since schema coverage is 100%, the schema already explains each parameter's format and purpose. The description adds significant semantic value by linking parameters to business rules (e.g., delivery within 15 miles via zip, delivery is week-ahead only, same-day items are always full-size). This clarifies parameter interplay and helps the agent infer correct values (e.g., when to pass 'channel'). It enriches, rather than repeats, the schema information.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description states the exact purpose: 'Check a basket against the bakery's ordering rules BEFORE any checkout.' It also enumerates what it returns (lane, priced lines, subtotal, problems, available dates/times), clearly distinguishing itself from menu-lookup tools (get_menu, search_menu, show_item) and the actual checkout (start_checkout). The 'BEFORE' keyword explicitly positions it as a pre-check step, making its role 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 explicitly directs 'BEFORE any checkout', implying it should be used prior to start_checkout. It also provides detailed business rules that dictate when each lane applies (same-day vs catering), giving the agent context to decide if this tool is appropriate. It does not explicitly name alternatives like 'use get_menu for menu items', but the sibling names and the clear scope make the appropriate use case evident. The instruction to 'show the customer the lines and the subtotal' also guides post-call behavior.

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 already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false, covering the safety profile. The description adds valuable behavioral context: data comes from live bakery data, sold-out items are marked in the today lane, and the strict rule to never quote prices/items not in results. This goes beyond the annotations.

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, tightly packed with no filler. The core purpose (menu with prices, photos, live data) is front-loaded, followed by lane details and a final constraint. 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?

With an output schema present and only two optional parameters, the description covers the essential usage: tool purpose, lane semantics, and the critical constraint about quoting only valid results. It's sufficient for an agent to call this correctly, though it doesn't cover edge cases like handling invalid section filters.

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 (lane enum with default, section with example). The description enriches the lane parameter by explaining the exact meaning and purpose of each lane, including size options for catering and sold-out marking for today. This adds 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 it's a menu browsing tool with prices and photos from live data, and explains the two distinct lanes (today's counter menu vs catering catalogue). It implicitly differentiates from siblings like search_menu by being the general browsing entry point, though it doesn't name alternatives 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 clear guidance on when to use each lane (today for same-day pickup, catering for advanced orders), which is useful context. However, it doesn't explicitly contrast with sibling tools like search_menu or show_item, leaving some inference needed for tool selection.

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

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

Annotations already declare readOnlyHint=true, destructiveHint=false, and idempotentHint=true, so the safety profile is known. The description adds valuable behavioral context beyond annotations: it specifies that each match includes price, description, photo URL, and allergens. More importantly, it gives the agent a directive on how to handle unresolved allergies (tell the customer and provide the phone number), which is essential behavioral guidance not derivable from annotations.

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, with the core purpose and scope first, followed by the result contents and the allergy-handling instruction. Every sentence earns its place—no filler, no repeated schema info. The structure is efficient and front-loaded with the actionable verb and resource.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a search tool with two parameters (one required), a complete output schema, and annotations covering read-only safety, the description covers everything an agent needs: what to search, where to search, what is returned, and how to handle a common edge case (unresolved allergen). The phone number even addresses escalation. Nothing critical is missing.

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 both parameters (query and lane) are already documented. The description reinforces that query is a few words and implies lane scope via 'today's counter menu and catering,' but does not add new syntax or format details. Since the schema fully covers parameter meanings, a baseline of 3 is appropriate; the description adds minimal semantic 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 a specific verb ('Find') and resource ('menu items') with a precise scope ('today's counter menu and catering'). It distinguishes from siblings like get_menu (full menu) and show_item (single item) by focusing on keyword search across both menu segments. The mention of return fields (price, description, photo URL, allergens) further clarifies the tool's purpose.

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 a few words like a filling, pastry, 'vegan', or 'ube' are given to search the menu. It also provides clear guidance on allergen handling (if allergy not settled, say so and point to phone). However, it does not explicitly name alternatives or exclusions, though the purpose implicitly distinguishes it from siblings. This is clear context without explicit 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?

Annotations already provide readOnly, idempotent, and non-destructive hints. The description adds meaningful behavioral context beyond annotations: the photo is included, allergen details are surfaced, and the agent is told to direct allergy concerns to a phone number. No contradiction with annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is compact and front-loaded with the core purpose, then usage, then an important allergen note. Minor redundancy exists—'The photo is included in the result' repeats what the first sentence already states—but overall it is efficient and earns its length.

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 output schema exists, the annotations cover safety traits, and both parameters are fully documented, the description supplies the remaining essential context: when to invoke it and how to handle allergen-related customer concerns. Nothing critical is missing for correct tool invocation.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so both 'name' and optional 'lane' are already documented in the schema. The description does not add additional parameter-level semantics beyond what the schema provides, so the 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 states a specific verb and resource: shows one menu item with photo, description, sizes, prices, and allergens. This clearly differentiates it from likely siblings like get_menu (whole menu) or search_menu (finding items) by scoping it to a single item.

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 an explicit trigger: 'Use it when the customer asks what something is or wants to see it.' It does not name alternatives or exclusions, but the usage context is clear enough for an agent to select it appropriately 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?

Beyond the annotations, the description discloses that no payment happens, that same-day links expire after 3 hours, that the order is re-validated, and that rule violations return problems rather than a link. This gives the agent a strong model of side effects and failure outcomes.

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 information-dense and front-loaded with the precondition, and the same-day/catering contrast is efficient. It is slightly long and contains some redundancy ('checks the same order again' after mentioning check_order), but it is not padded.

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 11-parameter tool with an output schema, the description covers the workflow gate, channel variants, link validity, no-payment behavior, and failure mode. There is no missing context an agent needs to decide whether and how to call it.

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 documents all 11 parameters with 100% coverage, so the description need not repeat them. It adds contextual flavor for date/time/fulfillment in catering but no parameter-level detail beyond the schema, matching the baseline for full 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 states a specific action ('checks the same order again and returns the link the customer pays on') and a clear resource, distinguishing it from check_order. The same-day/catering branches make the tool's function concrete without ambiguity.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The opening clause gives an explicit precondition: only call after check_order returned allowed and the customer confirmed lines/subtotal. It also states the failure behavior (returns problems instead of a link), which tells the agent when the result is not usable.

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