Punk MCP. Filter BrewDog Beer Data & Pairing Suggestions
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Punk lets your AI client access the full BrewDog beer catalog data through a single API connection. You can search by specific ingredients like hops or malts, filter results using technical specs such as ABV range or IBU levels, and get instant food pairing suggestions for recipes or cuisines.
This is essential for homebrewers and chefs who need precise brewing data.
What your AI agents can do
Get beer
Retrieves the full details for a single beer when you provide its unique ID.
Get random beer
Fetches an unexpected, random brew from the entire catalog to give you new ideas.
List beers
Searches and filters multiple beers using criteria like ABV, IBU, ingredients, or food pairings across the whole database.
The agent searches the catalog using precise numerical ranges for ABV, IBU, or EBC values.
It recommends specific brews designed to complement a given dish or type of cuisine.
You can query the database by filtering for specific hops, malts, or yeast strains used in brewing.
It pulls all available recipes and technical data for a single beer when you provide its unique ID.
The agent lists multiple beers, allowing you to apply any combination of filters (ingredients, pairings, specs) at once.
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Supported MCP Clients
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Punk MCP Server: 3 Tools for Brewing Data
Use these three tools to search, filter, and list beers using ingredient data, technical specs, and pairing information from the BrewDog catalog.
019e5d4bget beer
Retrieves the full details for a single beer when you provide its unique ID.
019e5d4bget random beer
Fetches an unexpected, random brew from the entire catalog to give you new ideas.
019e5d4blist beers
Searches and filters multiple beers using criteria like ABV, IBU, ingredients, or food pairings across the whole database.
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What you can do with this MCP connector
Punk lets your AI client access the full BrewDog beer catalog data through three precise tools: list_beers, get_beer, and get_random_beer.
When you run a search, you can list multiple beers across the entire database using list_beers by applying any combination of technical criteria or flavor profiles. You don't just get a simple list; your agent uses that tool to cross-reference specs like ABV range, IBU level, and EBC color metrics simultaneously.
Need to narrow down thousands of options? You can filter the entire catalog using precise numerical ranges for Alcohol by Volume (ABV), International Bitterness Units (IBU), or Evenness Color Scale (EBC) values. Specify a minimum ABV if you're looking for something strong, or set an EBC range if your recipe calls for a specific color profile—it handles the math instantly.
If you're planning a meal, list_beers recommends specific brews designed to complement any cuisine type or dish you name. You can search by food pairings to find beers that hit the right notes with whatever you've got on the menu. You don't have to guess; the tool finds the matches for things like Italian dinners or spicy Thai curries.
Flavor analysis is just as simple. Use list_beers to query the database by filtering specifically for particular hops, malts, or yeast strains. Want a beer that uses Chinook hops? You ask for it, and the tool pulls every match. Need a specific malt like crystal or caramel? The agent filters out everything else.
Once you've narrowed down your search results using list_beers, you can drill down into any single brew using the get_beer tool. Just give it that unique ID, and the tool pulls every bit of data available for that one beer. You get all the technical measurements, detailed recipe notes, and brewing tips—everything a homebrewer needs to know before they start boiling water.
If you've run through every single listing and just need some inspiration, get_random_beer fetches an unexpected brew from the whole catalog. It’s perfect for when you’re stuck and need a totally new idea to throw into your next project. You don't have to dig; it hands you something random but solid.
This server lets you use your AI client like a professional brewer’s reference guide, handling complex data requests that would take hours otherwise. You can combine technical specifications with ingredient requirements or food pairings in one shot. It’s built for chefs and brewers who need absolute accuracy when they're working.
How Punk MCP Works
- 1 Subscribe to the server and enter 'OPEN' in the token field. This confirms your client connection.
- 2 Your AI agent sends a structured query (e.g., "List beers that pair with spicy curry.") using one of the available tools.
- 3 The MCP Server executes the search against the BrewDog API and returns the filtered data, which your agent then presents to you.
The bottom line is: You talk naturally to your AI client, and it translates that into a precise database query using Punk.
Who Is Punk MCP For?
Anyone who works with detailed formulas or curated collections. This means professional chefs planning menus, homebrewers running research notes, or app developers building any consumer-facing product around beverage data.
Uses the agent to find specific beer pairings for a new seasonal menu item that needs to complement both spicy and sweet dishes.
Needs to cross-reference hop profiles and malt bills quickly, checking if existing recipes fit certain target ABV or IBU goals without leaving their research document.
Tests how the agent handles complex data structures, such as listing beers while filtering by multiple criteria (e.g., 'Show me all malts under 5 EBC with >6% ABV').
What Changes When You Connect
- Targeted brewing data: Instead of sifting through PDFs, you use
list_beersto filter by precise metrics—like needing a beer between 5.5% and 6.0% ABV. - Instant meal pairing: Need to know what brews go with spicy curry? Ask the agent; it runs the query and suggests specific IDs like 'Punk IPA' (ID: 192).
- Deep ingredient research: Homebrewers can use
list_beersto search only for beers that contain a specific hop, skipping all irrelevant results. - Quick lookup of specs: If you know the ID, use
get_beerto immediately pull up full recipes and technical measurements without running complex searches. - Creative inspiration: Stuck in a rut? Just call
get_random_beerto let the agent throw out a suggestion that might inspire your next project.
Real-World Use Cases
Creating a Menu Pairing Guide
A chef is building a new menu. They tell their agent: "Find me three beers that pair with spicy meat pizza." The agent runs list_beers, filters by cuisine, and returns specific recommendations (like 'Punk IPA') complete with details.
Cross-Referencing Malt Bills
A homebrewer is designing a new recipe. They ask the agent to list all beers using 'Chinook' hops that have an ABV under 6%. The agent executes list_beers, filters by ingredient and range, solving the research problem in seconds.
Quick Recipe Check
A developer needs to verify a beer spec for a client. They know the ID (192). Instead of searching, they use get_beer with the ID and get all specs instantly: 'Punk IPA,' 5.6% ABV.
Finding a Strong Draft Beer
A user wants an unexpectedly powerful beer to sample. They ask for a random brew with high metrics. The agent calls get_random_beer and returns 'Hardcore IPA' (ID: 15), showing it has 9.2% ABV and 125 IBU.
The Tradeoffs
Using the wrong tool for filtering
The developer tries to find all beers pairing with spicy curry by calling get_beer and passing 'spicy curry' as the ID. The agent fails because IDs must be numeric.
→
Use list_beers. This tool accepts complex filters like food pairings, allowing you to search the entire catalog for matches without knowing a single beer ID.
Over-relying on randomness
The user asks for 'a good IPA' and just runs get_random_beer. They get a 3% light lager, which is useless for their high-bitterness goal.
→
Always use list_beers and include filter parameters like IBU or ABV to narrow the search immediately. Never assume randomness matches your criteria.
Ignoring single lookups
The user knows a beer ID (192) but tries running list_beers with only that ID and no filters, wasting time processing unnecessary data.
→
If you have the exact identifier, use get_beer. It's faster and cleaner than using the listing tool just to pull one record.
When It Fits, When It Doesn't
Use this server if your task involves querying structured beer data—specifically searching by ingredients, technical measurements (ABV/IBU), or food pairings. Use list_beers when you need to filter a group of results (e.g., 'Show me all high-malt beers under 7% ABV'). Only use get_beer if you have the exact ID number and only want that one beer's full specs. If you just need some inspiration, call get_random_beer. Don't use this server if your goal is conceptual; for example, if you are asking 'What makes a good pairing?'—that’s general knowledge, not a database query. You need data to run these tools.
Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Punk API. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.
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Works with Claude, ChatGPT, Cursor, and more
The Model Context Protocol standardizes how applications expose capabilities to LLMs. Instead of operating in isolation, your AI gains direct access to external platforms, live data, and real-world actions through secure, standardized connections.
This server provides 3 capabilities that interface natively with Claude, ChatGPT, Cursor, and any MCP client. No middleware. No custom integration required.
Available Capabilities
Finding specific brewing specs used to take hours of spreadsheet work.
Before this server, figuring out which brews matched criteria was painful. You'd have to jump between the official BrewDog website, download multiple PDFs for ingredient lists, and manually cross-reference ABV ranges in a giant spreadsheet. It was slow, prone to human error, and you often missed key details.
Now, your agent handles it all. Instead of clicking through ten tabs or spending hours cleaning up data, you simply ask: "List beers with high IBU that pair with spicy food." The server runs the query and gives you a clean list right away.
Punk MCP Server: Filter beer data using list_beers.
Manually filtering through thousands of product entries based on multiple criteria—like 'must have Chinook hops' AND 'must be under 6 EBC'—used to require writing complex, custom scripts just to handle the API endpoints. It was a massive time sink.
With `list_beers`, you talk to your agent naturally. You ask for the constraints in plain English, and the server translates that into the required filtering parameters. The result is clean data—no more manual checks or confusing multi-step workflows.
Common Questions About Punk MCP
How do I use `list_beers` to find beers for a specific meal? +
list_beers handles this by accepting food pairing criteria. You just need to tell the agent what cuisine or dish you're planning, and it filters the entire catalog accordingly.
Should I use `get_beer` or `list_beers` if I know an ID? +
If you only want details on that single beer (like its full recipe), use get_beer. If you need to check if other beers share similar specs, use list_beers with filters.
What is the difference between `get_random_beer` and filtering? +
get_random_beer gives you a single suggestion with no constraints. Filtering requires you to define specific criteria (like ABV > 7%) so the result meets your needs.
Can I use Punk MCP Server for brewing recipes? +
Yes, it provides full recipe details and technical measurements via get_beer. It's built around retrieving structured data from the BrewDog catalog.
What token do I need to use when calling `list_beers`? +
Since this is a public API, you simply enter 'OPEN' in the token field. You don't need complex authentication keys or OAuth credentials for basic access to filter the catalog.
Does `list_beers` support specific numeric ranges for metrics like ABV? +
Yes, you can pass precise numerical boundaries when listing beers. If you want a range—say, 5% to 6% ABV—the API accepts both minimum and maximum values in your query parameters.
If I use `get_beer` with an ID that doesn't exist, what happens? +
The system returns a standard 404 Not Found status code. This is helpful because your agent can reliably catch the error and prompt you to check your input ID.
Is there a rate limit I should worry about when calling `get_random_beer`? +
The API handles moderate traffic fine, but if you run through many requests in quick succession, you might hit standard rate limits. For high-volume use, check the full documentation for specific quota details.
How can I find beers that pair well with a specific food like 'steak'? +
You can use the list_beers tool and provide the food parameter with your dish name. The agent will return a list of beers from the BrewDog catalog specifically recommended for that pairing.
Can I filter beers by their alcohol percentage (ABV)? +
Yes! Use the list_beers tool with abv_gt (greater than) or abv_lt (less than) parameters to find beers within your preferred strength range.
How do I get the full recipe and details for a specific beer ID? +
Use the get_beer tool and provide the unique integer id. The agent will retrieve the complete profile, including ingredients, brewing methods, and technical specifications.
Use it with your favorite AI tools
Connect this server to Cursor, Claude, VS Code, and more.
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