Human-verified directory of non-alcoholic bars: 1,500+ venues in 70 cities, rated and re-checked.
Scored across 5 tools
Each tool has a unique, clearly defined purpose: finding drinks, fetching menus, retrieving venue details, listing cities, and searching venues. No overlapping functionality or ambiguity.
All tool names follow the same snake_case verb_noun pattern (find_drink, get_menu, get_venue, list_cities, search_venues), providing a predictable and consistent naming convention.
With five tools, the set is well-scoped for a directory service, covering the core read-only operations without unnecessary bloat or redundancy.
The tool set covers the full read-only lifecycle: searching for venues, drilling into a venue, viewing its menu, finding specific drinks, and listing supported cities. No obvious missing features for the stated domain.
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 644 venue-pours): na-ipa (124), na-lager (85), na-spritz (83), na-sparkling-wine (70), na-negroni (59), na-white-wine (29), kava-serve (24), 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 (6), na-aperol-spritz (5), na-moscow-mule (4), na-negroni-sbagliato (4), na-sour (4), na-highball (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 provided, the description carries the full burden and explicitly states 'Read-only'. It also discloses data provenance (verified menus only, never reviews/guesses), what is returned, and that unknown drinks fall back to the catalog, giving strong 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 longer than average due to the detailed drink catalog and counts, but that information is directly useful for valid parameter choices. There is no fluff or redundancy; each section serves a purpose, though the catalog list is somewhat dense.
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 search tool with only two parameters and no output schema, the description is highly complete. It explains the source of truth, the result contents, URL citation support, and the unknown-drink fallback, leaving little ambiguity for an agent.
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 already covers both parameters with helpful descriptions, and the tool description adds practical detail: accepted forms for drink names (slugs vs plain words), city formats, and the behavior for unknown drinks. This goes well beyond the baseline 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 verb ('Find') and resource ('venues whose VERIFIED menu lists a specific non-alcoholic drink'), with an optional city filter. This clearly differentiates the tool from siblings like get_menu and list_cities, and the drink-focused intent is 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 clear context: use this tool to find venues by verified drink availability, optionally limited to one city. It does not explicitly contrast with search_venues, but the drink-specific purpose is clear enough to infer the primary use case.
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 1541 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?
Beyond the lack of annotations, the description discloses key behavioral details: it is 'Read-only,' it caps results at 25 and sorts them ('sorted by NA Program Strength descending, then name'), and it returns a total match count. These details give a clear picture of what the tool does without hiding any side effects.
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 yet compact. It packs the dataset size, filter semantics, default behavior, sort order, output fields, and error handling into two sentences. No word is wasted, and the structure flows logically from overview to details to related usage.
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 three optional parameters and no output schema, the description covers all necessary context: what is returned (fields listed), pagination/capping ('Returns at most 25 venues'), sorting, and how to get the full record. It also includes an important caveat about exceeding the cap, making the tool's behavior predictable.
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 provides full parameter descriptions, and the tool description adds extra semantic guidance. For example, the city parameter is expanded with 'Call list_cities for the covered set — cities outside it return zero matches,' and min_program_strength is clarified with a concrete example ('E.g. 4 returns only the strongest programs'). This goes beyond 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 clearly states the tool's purpose: 'Search NABarFinder — a human-verified directory of 1541 venues...' It identifies the resource (the directory) and the action (search). It also distinguishes from siblings by mentioning 'Use get_venue with a result's slug for the full record,' providing a clear alternative for follow-up.
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 explains when and how to use the tool: 'All filters are optional and combine with AND; with no filters it returns the top-rated venues overall.' It also provides guidance on handling large result sets ('if it exceeds the cap, narrow with filters') and directs users to a related tool for full details ('Use get_venue...'). The 'Read-only' warning sets expectations for side effects.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
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