Untappd Model Context Protocol (MCP) Server: Powering AI Agents with Beer Data

The Untappd Model Context Protocol (MCP) Server bridges the gap between the Untappd API and AI agents, particularly those leveraging large language models (LLMs). MCP serves as a standardized protocol, streamlining how applications provide context to LLMs. This specific server allows AI agents to access and utilize Untappd’s vast database of beer information, enabling a range of innovative applications.

However, a crucial caveat exists: Untappd is, unfortunately, no longer accepting registrations for new API keys. This limitation underscores the importance of responsible API usage and the potential fragility of relying on external data sources.

Use Cases: Unleashing the Potential of Beer-Aware AI Agents

While the API key limitation presents a challenge, the potential use cases for a functional Untappd MCP Server are compelling. Here are several examples:

• Intelligent Beer Recommendation Engines: An AI agent can analyze a user’s past Untappd check-ins and preferences (styles, breweries, ABV, IBU) to provide highly personalized beer recommendations. This goes beyond simple filtering and leverages the power of AI to discover hidden gems the user might enjoy.
• Contextual Beer Pairings: Imagine an AI agent that understands the food a user is eating and suggests the perfect beer pairing based on flavor profiles, ingredients, and regional cuisines. This could be integrated into restaurant ordering systems or home cooking applications.
• Automated Beer Event Discovery: An AI agent can monitor Untappd check-ins and user activity to identify trending beers and breweries in a specific geographical area. This information can be used to create automated event recommendations or to alert users to limited-release beers.
• Enhanced Beer Information Retrieval: The server provides a structured way for AI agents to answer complex questions about beer. For example, a user could ask, “What are some highly-rated IPAs from breweries in San Diego?” and the AI agent could retrieve the information and present it in a natural language format.
• Social Beer Discovery: An AI agent can analyze a user’s social network on Untappd to identify beers that their friends have enjoyed and recommend them. This leverages the power of social influence to drive beer discovery.
• AI-Powered Beer Reviews and Descriptions: The data obtained can be used to train models that can generate descriptions of beers based on style, ingredients, and user reviews. This allows for more automated content generation and improved product discoverability.
• Beer Tourism Planning: AI agents can help plan beer-centric travel itineraries, recommending breweries, beer bars, and beer festivals based on user preferences and location.

Key Features and Functionality

This MCP Server currently supports three core functionalities:

1. search_beer: This tool allows AI agents to search the Untappd database for beers based on keywords, style, brewery, or other criteria. The search results provide a list of beers with their basic information, including beer ID, name, brewery, and style.
2. get_beer_info: This tool retrieves detailed information about a specific beer using its unique beer_id obtained from the search_beer tool. The detailed information includes the beer’s description, ABV, IBU, ratings, and other relevant data.
3. get_user_checkins: (Currently Non-Functional) This tool aims to retrieve a user’s check-in history on Untappd. This functionality is currently not working, but if operational, it would allow AI agents to analyze a user’s beer preferences and provide personalized recommendations.

Technical Details and Implementation

The Untappd MCP Server is written in Node.js, a popular JavaScript runtime environment. This choice allows for efficient development and deployment on various platforms. The server utilizes the Untappd API to retrieve beer data. However, as previously mentioned, access to the Untappd API requires an API key, which is no longer available for new registrations.

Development and Installation

To set up and run the server locally, follow these steps:

1. Install Dependencies: Use the command npm install to install the necessary Node.js modules.

2. Build the Server: Use the command npm run build to compile the TypeScript code into JavaScript.

3. Development with Auto-Rebuild: Use the command npm run watch to automatically rebuild the server whenever changes are made to the code. This is useful for development and testing.

4. Installation with Claude Desktop: To integrate the server with Claude Desktop, you need to add the server configuration to the claude_desktop_config.json file. The location of this file varies depending on your operating system:

   • MacOS: ~/Library/Application Support/Claude/claude_desktop_config.json
   • Windows: %APPDATA%/Claude/claude_desktop_config.json

5. Add the Following Configuration:

   { “mcpServers”: { “untappd-server”: { “command”: “/path/to/untappd-server/build/index.js” } } }

   Replace /path/to/untappd-server/build/index.js with the actual path to the server’s executable file.

Debugging

Debugging MCP servers can be challenging due to their communication over standard input/output (stdio). The recommended approach is to use the MCP Inspector, which provides a web-based interface for inspecting the communication between the AI agent and the server.

To use the MCP Inspector, run the command npm run inspector. This will provide a URL to access the debugging tools in your browser.

The Importance of MCP and the UBOS Platform

The Model Context Protocol (MCP) is a critical enabler for building powerful and versatile AI agents. By standardizing how applications provide context to LLMs, MCP simplifies the integration of AI agents with external data sources and tools. This allows AI agents to access real-world information and perform complex tasks.

The UBOS platform further enhances the development and deployment of AI agents by providing a comprehensive suite of tools and services. UBOS focuses on bringing AI Agent capabilities to every business department. It simplifies the orchestration of AI Agents, enables seamless connection with enterprise data, facilitates the building of custom AI Agents using your preferred LLM models, and supports the creation of sophisticated Multi-Agent Systems.

By leveraging UBOS in conjunction with MCP-enabled servers like the Untappd server (if API access were readily available), developers can create innovative AI-powered solutions that transform industries and improve people’s lives. The UBOS platform’s strength lies in its ability to handle the complexities of AI Agent development, allowing businesses to focus on creating value and achieving their strategic goals.

Limitations and Future Directions

The primary limitation of this Untappd MCP Server is the restricted access to the Untappd API. Without a valid API key, the server cannot retrieve data and its functionality is severely limited. Future development efforts should focus on exploring alternative data sources or working with Untappd to potentially regain API access.

Despite this limitation, the Untappd MCP Server serves as a valuable example of how MCP can be used to integrate AI agents with external data sources. It highlights the potential for building intelligent applications that leverage the power of AI to provide personalized recommendations, automate tasks, and improve decision-making. With the UBOS platform, the process of building and deploying such applications is greatly simplified, enabling businesses to unlock the full potential of AI.