Human-verified directory of non-alcoholic bars: 1,500+ venues in 70 cities, rated and re-checked.
Scored across 5 tools
Each tool targets a distinct resource and action: find_drink searches by drink kind, search_venues searches venues by filters, get_menu and get_venue retrieve different details for a single venue by slug, and list_cities enumerates cities. There is no meaningful overlap, and descriptions clearly delineate when to use each.
All tool names follow a consistent verb_noun pattern in snake_case: find_drink, get_menu, get_venue, list_cities, search_venues. The convention is uniform and predictable.
Five tools form a well-scoped set for a read-only directory: listing cities, searching venues, retrieving venue details, retrieving menus, and finding specific drinks. No tool feels redundant or missing at this count.
The surface covers the core discovery workflows: cities, venues, menus, and specific drinks. Minor gaps exist, such as no way to browse all venues in a large city beyond the 25-result cap and no direct search by NA brand, but agents can work around these by narrowing filters or using get_venue.
Find venues whose VERIFIED menu lists a specific non-alcoholic drink — e.g. a real NA negroni, non-alcoholic IPA, zero-proof espresso martini, NA spritz or kava serve — optionally in one city. Every result comes from the venue's own published menu (read verbatim and dated), never from reviews or guesses, so a listed venue genuinely pours that drink. Returns the matching menu items (name, description, price where printed, on-draft flag), the menu's last-confirmed date, and the canonical nabarfinder.com URLs for citing. Drinks currently on menus (24 kinds across 640 venue-pours): na-ipa (125), na-lager (84), na-spritz (83), na-sparkling-wine (70), na-negroni (59), na-white-wine (28), kava-serve (23), na-mocktail-sour (22), na-mojito (20), na-margarita (19), na-espresso-martini (18), na-red-wine (18), na-stout (11), na-old-fashioned (10), na-paloma (10), na-gin-and-tonic (7), na-margarita-spicy (7), functional-adaptogen-cocktail (5), na-aperol-spritz (5), na-negroni-sbagliato (4), na-sour (4), na-highball (3), na-moscow-mule (3), na-wheat-beer (2). Read-only.
| Name | Required | Description | Default |
|---|---|---|---|
| city | No | Limit to one covered city — slug ('austin-tx'), name ('Austin') or 'Austin, TX'. Omit for every city. | |
| drink | Yes | The drink, as a slug ('na-negroni') or plain words ('non-alcoholic negroni', 'NA IPA', 'virgin mojito'). Unknown drinks return the catalog of known slugs. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden and does so well: it declares read-only, explains the data provenance (read verbatim from the venue's own dated published menu, never reviews or guesses), and enumerates the return contents including the last-confirmed date and citable URLs. Only auth requirements and result limits/pagination are unaddressed, which is minor for this read-only lookup.
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?
Purpose is front-loaded, followed by provenance, then a concrete return-value list — good ordering with little filler. The 24-slug catalog with counts is lengthy and partially redundant with the schema's note that unknown drinks return the catalog, but it does earn its place by steering parameter choice.
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 two-parameter, no-output-schema, no-annotation tool, the description supplies everything needed to call it correctly: what it searches, what data backs it, the shape of the results, and the read-only nature. City coverage gaps are reasonably delegated to the list_cities sibling.
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, and the description goes further by listing the 24 known drink slugs with pour counts and giving plain-word aliases, which materially helps the agent choose a valid 'drink' value. The 'city' parameter's slug/name/'Austin, TX' formats are handled by the schema description rather than the tool 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 precise verb and resource: find venues whose VERIFIED menu lists a specific non-alcoholic drink, optionally scoped to one city. The drink-centric, verified-menu-only framing cleanly separates it from venue-oriented siblings like search_venues and get_venue, so an agent can route without opening a schema.
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 is implied rather than prescribed: the 'optionally in one city' note and the catalog of currently available drinks give context for when it applies. However, it never states when NOT to use it or points to an alternative (e.g. get_menu for a venue's full menu, search_venues for non-drink discovery), leaving routing to inference.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Fetch the full verified record for one NABarFinder venue by its stable slug: city, neighborhood, address and coordinates (when it has a fixed location), venue type, naProgramStrength (1–5), full description, NA brands stocked (brandSlugs), last-verified date, canonical nabarfinder.com URL, and — when the registry has them — website, phone, hours, signature drinks, and a sourced press quote. Fields absent from the registry are omitted, never invented. Read-only.
| Name | Required | Description | Default |
|---|---|---|---|
| slug | Yes | The venue's stable slug id, e.g. 'sans-bar-austin' — as returned by search_venues or the /api/v1/venues.json feed. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden, and it delivers: it explicitly states that fields absent from the registry are omitted and never invented, and that the operation is read-only. This goes well beyond the schema by setting accurate expectations about absent data and data integrity.
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 names the action and resource, then lists the returned fields. It is dense but every clause adds useful information, and because there is no output schema, the length is justified.
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 only one parameter, no annotations, and no output schema, the description is remarkably complete: it enumerates the full set of return fields, explains omission behavior, and declares the read-only nature. An agent can reliably decide to invoke it and interpret its results without needing additional context.
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 fully documents the lone slug parameter with an example and its source ('as returned by search_venues or the /api/v1/venues.json feed'), so schema coverage is 100%. The tool description repeats 'stable slug' but adds little parametric meaning beyond what the schema already provides, so the baseline score 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 opens with a specific verb and resource: 'Fetch the full verified record for one NABarFinder venue by its stable slug.' It enumerates the exact fields returned and references search_venues as the source of the slug, which clearly distinguishes this single-venue lookup from sibling tools like search_venues and list_cities.
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 context: this is a single-venue lookup keyed by a stable slug, and it tells the agent the slug comes from search_venues or the /api/v1/venues.json feed. It implies when to use it, but it does not explicitly state exclusions or alternatives such as 'use search_venues when you need a list.'
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
List every city NABarFinder covers, each with its state, number of human-verified venues, city slug (for search_venues), and canonical nabarfinder.com city guide URL. Takes no input. Read-only.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are present, so the description carries the full burden. It explicitly states 'Read-only' and 'Takes no input', which are key behavioral details. It does not mention potential side effects or limitations, but the read-only nature makes such omissions acceptable.
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 communicates the purpose, output content, and behavior (read-only, no input). It is well-structured with no redundancy.
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, parameterless list operation, the description is complete. It covers the purpose, output fields, and behavioral constraints (read-only), making it sufficient for an agent to decide when and how to invoke the 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 tool has no parameters, so schema coverage is effectively 100% (nothing to cover). The baseline is 3, and the description adds no parameter-specific information because none exist.
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 function (list all cities NABarFinder covers) and specifies the output fields (state, number of human-verified venues, city slug, canonical URL). It distinguishes from sibling tools by focusing on cities rather than individual venues or searches.
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 it (when a full list of cities is needed) and notes it takes no input, but it does not explicitly contrast with the sibling tools or state an alternative for filtering. The implied usage is clear enough.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Search NABarFinder — a human-verified directory of 1548 venues with real non-alcoholic (NA) drink programs across 70 cities in the US, UK, and beyond. All filters are optional and combine with AND; with no filters it returns the top-rated venues overall. Returns at most 25 venues (sorted by NA Program Strength descending, then name), each with name, slug, city, venue type, naProgramStrength (1–5; 5 = fully alcohol-free or a dedicated NA program, 1 = a few NA options), a one-line summary, and the canonical nabarfinder.com page URL for citing/linking. The response includes the total match count; if it exceeds the cap, narrow with filters. Use get_venue with a result's slug for the full record. Read-only.
| Name | Required | Description | Default |
|---|---|---|---|
| city | No | Limit to one covered city. Accepts a city slug ('austin-tx'), a plain name ('Austin'), or 'Austin, TX'. Call list_cities for the covered set — cities outside it return zero matches. | |
| query | No | Free-text filter over venue name, neighborhood, venue type, and description. Case-insensitive; every word must match (e.g. 'kava lounge', 'zero proof cocktail'). | |
| min_program_strength | No | Minimum NA Program Strength on the registry's 1–5 scale (5 = fully alcohol-free / dedicated NA program). E.g. 4 returns only the strongest programs. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden and delivers: read-only status, the hard 25-result cap, sort order, the presence of a total match count, and the exact per-result fields. It also warns that cities outside the covered set return zero matches.
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?
Front-loaded with identity and scope, and every sentence carries information (cap, ordering, fields, citations, routing). It is delivered as one dense block rather than scannable segments, which slightly hurts structure but not content economy.
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, so the description must describe returns — and it does, naming each field, the cap, ordering, and the total match count. Combined with the zero-required-parameter invocation model and sibling routing, an agent has everything needed to call and interpret this 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 description coverage is 100%, so the schema already documents city slug/name formats, the free-text AND-matching behavior, and the 1–5 strength scale. The description largely restates the strength scale meaning rather than adding new parameter-level guidance, so the baseline 3 applies.
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 and resource (search a human-verified directory of 1548 NA venues) plus scope (70 cities across US/UK) and result semantics (max 25, sorted by NA Program Strength). It explicitly distinguishes itself from siblings by routing full records to get_venue and the covered city set to list_cities.
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?
Gives explicit conditions: all filters are optional, combine with AND, no filters returns top-rated overall, and when the total match count exceeds the cap the agent should narrow with filters. It also names the follow-up tools (get_venue by slug, list_cities for the covered set) with the reason to use each.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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