How to Use the CocktailFyi MCP in Mastra AI

Build complex bartending workflows in Mastra AI with automated error handling and tool-based logic.

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Connect CocktailFyi MCP to Mastra AI

Create your Vinkius account to connect CocktailFyi to Mastra AI and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

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Setup CocktailFyi with Mastra AI

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Automate drink workflows with Mastra AI

Chain `get_cocktail` and `get_ingredient` inside your Mastra agents to build multi-step drink recommendations. If a specific ingredient is missing, your workflow can automatically branch to find a suitable substitute. This architecture keeps your logic modular. You define the path, and the server provides the data needed to keep the agent moving forward.

Filter categories for agent tasks

Use `list_categories` and `get_cocktails_by_category` to scope your agent's knowledge. You can restrict your agent to only suggest specific types of drinks based on user preferences or inventory availability. It prevents the agent from hallucinating outside the defined scope. Your agent stays focused on the data provided by the server.

Manage bartending logic

The `list_techniques` tool lets your agent explain complex processes like shaking or muddling. Your workflow can trigger these descriptions when a user asks for help with a specific step in a recipe. It adds a layer of depth to your agent's responses. You get granular control over when the user receives technical guidance.

Setup guide

Set up CocktailFyi MCP in Mastra AI

Prerequisites

Node.js 18+ and a TypeScript project
@mastra/mcp + @mastra/core packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run npm install @mastra/mcp @mastra/core plus your preferred model provider (e.g. @ai-sdk/openai).
2

Configure the MCPClient
Create an MCPClient with your Vinkius endpoint as a URL object. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Discover and inject tools
Call mcpClient.listTools() and spread the result into your agent's tools object. All CocktailFyi tools become native Mastra tools.
4

Run with any model
Swap openai("gpt-4o") for any AI SDK-compatible provider. Call agent.generate() and the agent routes tool calls through MCP automatically.

agent.ts

import { MCPClient } from "@mastra/mcp";
import { Agent } from "@mastra/core/agent";
import { openai } from "@ai-sdk/openai";

const mcpClient = new MCPClient({
  id: "cocktailfyi-mcp-client",
  servers: {
    "cocktailfyi-mcp": {
      url: new URL(
        "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
      ),
    },
  },
});

const agent = new Agent({
  name: "CocktailFyi Agent",
  model: openai("gpt-4o"),
  instructions: "You have access to CocktailFyi tools.",
  tools: {
    ...(await mcpClient.listTools()),
  },
});

const result = await agent.generate(
  "List recent CocktailFyi transactions"
);
console.log(result.text);

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by CocktailFyi. 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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Common questions about CocktailFyi MCP in Mastra AI

Mastra allows you to set up retries or fallback logic if a tool call fails. You can define how the agent behaves if the CocktailFyi data is temporarily unavailable.

You can absolutely branch your workflow based on the ABV or difficulty returned by the tools. This lets you build smart logic that adapts to the specific drink retrieved.

Mastra agents run in your environment, and the server communication is encrypted. Your recipe requests are handled through secure transport tunnels.

Your queries are processed only to fetch the requested cocktail info. The server does not retain records of your agent's specific activity or user inputs.

Ingredient data is provided directly from the database and is never shared between different instances. Your session remains strictly private within the ephemeral execution environment.

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