How to Use the Hydration Calculator MCP in Mastra AI

Q: What data does Hydration Calculator require for determining dough type with Mastra AI?

The determinedoughtype function needs the full salt composition and flour makeup. These are the inputs necessary to classify if a recipe is meant for bread, pizza, or focaccia.

Build complex baking workflows with Mastra AI's guaranteed retries and branching logic.

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Setup Hydration Calculator with Mastra AI

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Complex Hydration Validation

If your initial calculation fails, your agent can automatically retry the hydration check. You pass flour and liquid weights to `calculate_hydration`, and Mastra AI handles retries with exponential backoff until it gets a valid dough mix percentage. This is critical for production systems where input data might be flaky or unreliable. The built-in workflow engine ensures your process doesn't just fail; it attempts recovery.

Conditional Recipe Branching

You can build branching logic based on the output of `determine_dough_type`. If the tool determines a 'pizza' dough type, the workflow executes one branch; otherwise, it follows another. This handles complex recipe requirements gracefully. It allows your agent to manage multiple outcomes without needing extensive manual error handling code.

Guaranteed Texture Assessment

After calculating hydration, use `classify_texture` to assess the crumb structure. This tool helps confirm if the dough profile is suitable for commercial baking standards. The MCP result feeds directly into your multi-step workflow. Because of Mastra AI's reliable execution, you can trust that the texture assessment will complete as part of a larger business process.

Setup guide

Set up Hydration Calculator 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 Hydration Calculator 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: "hydration-calculator-mcp-client",
  servers: {
    "hydration-calculator-mcp": {
      url: new URL(
        "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
      ),
    },
  },
});

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

const result = await agent.generate(
  "List recent Hydration Calculator 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 Hydration Calculator. 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 Hydration Calculator MCP in Mastra AI

You initiate the tool call within your workflow engine, providing flour and liquid weights. The MCP executes the calculation, and the result feeds back into your conditional branching logic for further steps.

Yes, that's a core feature of the platform. You can configure automatic retries on tool failures, ensuring your overall baking workflow completes even if one step throws an error.

The `determine_dough_type` function needs the full salt composition and flour makeup. These are the inputs necessary to classify if a recipe is meant for bread, pizza, or focaccia.

Yes. The workflow engine supports complex state management and failure handling, meaning it manages non-standard inputs far better than a simple script would.

The system uses zero-trust proxies. The only data touched by this MCP is ingredient weights and flour compositions, and your keys are never stored on disk.

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