美食神仙籤筒

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

No annotations are provided, so the description carries the full behavioral burden. It does disclose meaningful output behavior: an original fortune poem plus a dish, and a conditional branch where Taiwan coordinates trigger a nearby-open-restaurant pick with Google Maps and Uber Eats links. However, it says nothing about the no-coordinates case, what happens when no open restaurants exist, or whether the draw is randomized/read-only.

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 dense sentence that front-loads the core purpose ('美食神仙籤筒：今天吃什麼？') before the mechanics and the conditional behavior. Nearly every clause earns its place, though the length makes it slightly clausal-heavy.

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 8 parameters, no annotations, and no output schema, the description carries a lot of weight. It covers purpose, draw inputs, return values, and the coordinate branch reasonably well, but leaves the 'people' parameter and the behavior when coordinates are absent unspecified.

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 50%, so the description must compensate. It meaningfully explains that deity/meal/category/budget drive the draw and that coordinates (lat/lon) switch on the restaurant lookup, which adds value over the raw schema. But the 'people' parameter is unexplained in both schema and description, leaving a gap.

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?

States a specific verb (抽籤/draw a fortune) and resource (what to eat today), plus the inputs it draws from (deity, meal, category, budget) and what it returns (original fortune poem, dish). The tool's divination nature makes it inherently distinguishable from its siblings, but the description never explicitly names or contrasts them.

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?

Describes what the tool does but gives no guidance on when to prefer it over find_nearby_restaurants, list_deities, or list_food_categories, nor any when-not conditions. Usage is only implied by the playful framing, not stated.

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 but does add real behavioral content: filtering by open status (or missing hours), distance sorting, and enrichment with Google Maps and Uber Eats links. It omits radius semantics, result limits/pagination, and coordinate format, which are meaningful gaps for a query 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?

A single dense sentence with no filler, front-loading the core action and location scope before the output enrichment. It is efficiently sized and every clause contributes, though the packing makes it slightly heavy to parse.

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, and the description usefully discloses what comes back (distance-sorted results with map and Uber Eats links) plus the open-status filter. The only real hole is the undefined radius parameter and coordinate precision, which keeps it from being fully complete for a 4-param, 0%-schema-coverage 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?

Schema coverage is 0%, so the description must compensate. It gives meaning to lat/lon (a coordinate) and category (food type) but never mentions the radius parameter, which is left totally undocumented in both schema and description. Three of four parameters gain semantics — partial compensation.

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+resource (查找店家) and scopes it precisely: a coordinate in Taiwan, matching a food category, open status, sorted by distance, with Maps/Uber Eats links. This clearly distinguishes it from siblings like list_food_categories (which only enumerates categories).

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?

Usage context is implied — find restaurants near a point — but there is no explicit when-to-use versus when-not guidance, and no sibling alternative is named (e.g., list_food_categories for primitives). The open-status clause hints at filtering intent but is not framed as 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 the full disclosure burden. It does describe the return content (ten named deities and their associated food styles), which is more than nothing for a read-only list. However, it says nothing about whether the list is static/fixed, whether results are ordered, or the response shape, leaving real behavioral 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?

A single sentence that front-loads the count and resource before the detail. No filler or redundant restatement of the name.

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 zero-parameter list tool with no output schema, enumerating the exact ten entries and their food-style theme effectively communicates the return contents. What is missing is confirmation of format or ordering, but the core is complete enough 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?

The tool takes zero parameters, so per the rubric the baseline is 4. There is nothing param-related for the description to clarify, and it correctly implies no input is required.

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 gives a specific verb (列出/list) plus resource (臺灣廟宇主神) and even enumerates the exact ten deities and that each carries a 'food style'. This clearly distinguishes it from siblings like find_nearby_restaurants or list_food_categories. It stops short of explicitly contrasting itself with those siblings, so it lands at 4 rather than 5.

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?

There is no statement of when to call this tool versus draw_food_fortune or list_food_categories, and no prerequisites or exclusions. The description only says what the tool returns, leaving any usage routing to inference.

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 behavioral burden. It partially discharges this by disclosing return content (附一般價格帶, typical price bands) and the filtering dimensions, but says nothing about data freshness, coverage limits, or whether results are categories vs restaurants.

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 dense sentence with the resource first and filters trailing; the parenthetical examples add genuine value for category recognition rather than filler. No wasted clauses, though it is a single run-on that could be split for scannability.

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 annotations and no output schema, the definition covers the essentials: what is listed, how it can be filtered, and what the results include (price bands). The gaps are the undocumented people parameter and absent sibling routing, but for a lightweight discovery tool this is largely 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 description coverage is 0%, so the description must compensate. It clarifies 餐別 (matching the meal enum) and explicitly frames budget as 總預算, resolving whether it is total vs per-person. But the people parameter is never mentioned, leaving one of three parameters undocumented in both schema and description.

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?

States a specific verb+resource (列出美食種類) with concrete examples (台式早餐、牛肉麵、火鍋…), so an agent immediately knows what the tool returns. However, it never distinguishes itself from sibling find_nearby_restaurants, which also involves food discovery, so the boundary is left to inference.

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 phrase 可依餐別與總預算篩選 hints at capability but offers no explicit when-to-use, when-not-to-use, or alternative routing (e.g. vs find_nearby_restaurants). An agent gets no decision criteria for selecting this tool over its siblings.

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