Vahada — Turkish drink prices

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 clarifies the tool ranks by 'cheapest standing price' (implying non-discounted pricing) and optionally filters by area, but it does not disclose what the output looks like (e.g., does it return venue names, prices, or a sorted list?), nor does it mention any limitations like data freshness or venue availability. The description is honest but thin on behavioral detail, which is expected for a simple ranking 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 two sentences with zero filler. The core action is front-loaded ('Rank venues by the cheapest standing price'), followed by the optional scoping and two illustrative examples. Every word earns its place, and the structure guides the reader from general capability to specific use cases.

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 tool with only three simple parameters and no output schema, the description is largely complete. It specifies the ranking criterion, the optional filter, and gives usage examples. However, since there is no output schema, it would be slightly more complete to mention the return format (e.g., a ranked list of venues with prices). The absence of that detail is a minor gap but not critical for an agent to call the tool 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 input schema already provides full descriptions for all three parameters (drink, area, limit), with 100% schema description coverage. The description adds contextual examples that reinforce how parameters are used ('beer in Kadıköy'), but it does not add new semantic meaning beyond what the schema states. Per the rubric, a baseline of 3 is appropriate when the schema does the heavy lifting.

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 ('Rank venues'), a specific criterion ('cheapest standing price'), and a resource ('drink category'), with optional geographic scoping. It also provides two concrete query examples that make the tool's intent unmistakable. It distinguishes itself from siblings like happy_hours_now (which focuses on time-limited deals) by explicitly noting 'standing price'.

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 clear example queries ('where is the cheapest beer in Kadıköy' or 'cheap cocktails in Beyoğlu') that effectively illustrate when to use the tool. It does not explicitly state when not to use it or name alternatives, but the examples plus the 'standing price' wording implicitly steer agents away from happy-hour queries. A small gap is the absence of an explicit exclusion for time-sensitive pricing, which a sibling like happy_hours_now might handle.

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. It discloses the empty-list behavior as a legitimate answer and mentions 'Turkey time' (timezone context). It does not cover error conditions, authentication, or response structure beyond time-left, but for a simple read tool the disclosure is adequate though not exhaustive.

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 three sentences, front-loading the core function first, then providing usage guidance, and finally a behavioral note. Every sentence earns its place 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?

For a read-only tool with two optional parameters and no output schema, the description covers the main purpose, usage intent, timezone, and empty-list behavior. It does not detail the exact return structure, but given the simplicity and schema coverage, it is sufficiently complete 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 100%, so both 'area' and 'limit' are already documented in the input schema. The description adds minimal parameter-specific context (e.g., 'Turkey time' relates to area scope) but does not materially enhance parameter understanding beyond what the schema provides, warranting the baseline score.

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 returns currently running happy hours ('Discounted drinks being served AT THIS MOMENT') with time remaining, scoped to Turkey. It distinguishes itself from siblings by emphasizing the 'right now' aspect and provides concrete example queries, making the purpose unmistakable.

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 usage scenarios ('is there a happy hour near me right now', 'cheap drinks in Beyoğlu tonight') that indicate when to call it. However, it does not explicitly mention alternatives or exclusions relative to sibling tools, so it stops short of a full when/when-not guide.

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 burden of behavioral disclosure. It adds context beyond the schema by revealing that the tool filters based on a coverage threshold ('enough priced venues') and returns a count per area. It does not explicitly state read-only behavior, but the listing nature and lack of mutation verbs imply it. This is adequate for a simple 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, front-loaded sentence that immediately states the purpose, then adds the usage directive. There is no redundant wording or fluff. Every word contributes to understanding what the tool does and when to call it.

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 tool with one optional parameter and no output schema, the description covers the essential aspects: what areas are returned, the criterion for inclusion, and the count per area. It could mention error scenarios or the meaning of 'enough', but these are not critical for a basic list operation. The description is complete enough for an agent to call 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 input schema already provides a clear description for the single optional parameter ('Filter to one city, e.g. 'Istanbul''), achieving 100% coverage. The tool description does not add any parameter-specific guidance, so the baseline of 3 applies – the schema does the heavy lifting.

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 ('list') and resource ('areas with price data') with a clear criterion: 'which Turkish districts and neighbourhoods Vahada has enough priced venues to answer for'. It also specifies the return value (number of venues per area). This is self-contained and distinct from sibling tools like venue_prices or price_index, which focus on actual prices rather than coverage.

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 the agent to 'Call this first when unsure whether an area is covered', which is a clear usage context. It does not name specific alternatives or exclusions, but the 'call this first' phrasing implies it is a preliminary check. Given the simplicity of the tool, this is sufficient 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 must carry the behavioral burden. It discloses that data is 'frozen in a monthly edition' (implying non-real-time) and are medians, which is helpful. However, it does not explicitly state it is read-only, nor does it mention any side effects, response format, or limitations beyond the monthly update. This is adequate but 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 two sentences with zero fluff. The first sentence states the core resource and nature, the second gives a direct usage example. It is front-loaded and each sentence earns its place, making it highly efficient.

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 has no parameters and no output schema, the description covers the essential aspects: what (median drink prices), where (Turkey and districts), and when (monthly edition). It does not specify the exact return structure, but for a simple index that is acceptable. The usage example also reinforces completeness. A minor gap is not describing the output format, but it's not critical.

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, so the schema trivially covers 100%. Per calibration, a 0-parameter tool gets a baseline of 4. The description adds no parameter-specific meaning (there is nothing to add), which is appropriate; no further explanation is needed.

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 provides the published Vahada Bira Endeksi, which is a monthly frozen set of median drink prices for Turkey and covered districts. It also gives a concrete usage example ('what does a beer cost in Turkey'), making the purpose unambiguous and distinguishing it from live price tools like cheapest_drinks or venue_prices.

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 says 'Use for...' with a specific query, giving clear guidance on when to call this tool. However, it does not mention when not to use it or contrast it with siblings like cheapest_drinks or happy_hours_now, so it lacks exclusions but provides a clear usage context.

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 exist, so the description carries the full disclosure burden. It does reveal scope (every standing price, happy hours with days/times) and the accepted identifier, but it does not mention lookup failure, matching behavior, or behavior when a venue has no happy hours.

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 short sentences with no filler. The output scope is front-loaded, followed by input format, and every clause contributes meaningfully.

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 one-parameter tool with no output schema, this is nearly complete: it specifies the result content and input format. It could add error or edge-case behavior, but the low complexity makes that a minor gap.

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%, so the baseline is 3. The description mostly repeats the schema's name-or-slug input, adding only the 'Vahada' brand detail and the single-venue context, which is marginal additional value.

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 resource: every standing drink price for a single venue, plus happy hours with their schedules. It distinguishes scope ('for one venue') from broader price tools, but it does not explicitly name a sibling or use a direct verb, so it stops short of a 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?

The phrasing implies use when you need the full drink price list for a specific venue, and the second sentence states the accepted input. However, it offers no explicit guidance on when to use this versus cheapest_drinks or happy_hours_now, and no exclusions or alternatives are mentioned.

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 burden. It discloses the ordering ('nearest first'), the output content (cheapest drink per category, distance), and the radius/geospatial nature. It does not mention whether it is a read-only operation, any rate limits, or caveats about data availability, which are notable gaps for a tool with zero annotation coverage.

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 with no waste. The core behavior is front-loaded, and the usage trigger follows immediately. 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?

For a geospatial query tool with no output schema, the description explains what is returned (venues, cheapest drink per category, distance) and when to use it. It does not cover pagination or output format details, but given the simplicity and the fact that limit is documented in the schema, this is fairly complete. A brief note on result count limits would improve 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?

Schema coverage is 50%, with limit and radius_km documented. The description adds geospatial context (radius of a latitude/longitude) that clarifies the coordinate parameters implicitly, but it does not add specific format or syntax details beyond the schema. It helps a bit but does not fully compensate for the missing descriptions of latitude and longitude.

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 what the tool does: returns priced venues within a radius of a latitude/longitude, sorted nearest first, with cheapest drink per category and distance. It is specific about the resource and behavior, but it does not explicitly contrast with sibling tools like venue_prices or price_index, leaving some ambiguity about when to pick it over 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?

The description gives an explicit trigger: 'Use when the user has shared a location or named a place you can geocode yourself.' This is clear context for when to call it. However, it does not mention when not to use it or name alternatives, so the guidance is one-sided but still useful.

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