How to Use the Float MCP in Mastra AI

Q: Does Float work with Mastra AI multi-step processes?

Absolutely. You can pass the output of listpeople into a second tool like getperson to drill down into specific resource availability for complex scheduling tasks.

Build complex resource workflows in Mastra AI using the Float MCP Server for reliable scheduling automation.

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…and any MCP-compatible client

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Connect Float MCP to Mastra AI

Create your Vinkius account to connect Float 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 Float with Mastra AI

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Automated scheduling logic for Mastra AI

Chain `list_time_offs` and `create_allocation` to build a robust scheduling loop. If an agent detects a conflict, it can automatically suggest a new slot. This is how you handle scheduling at scale. You define the business rules in your workflow, and the MCP Server handles the actual write operations to the calendar.

Conditional project tracking

Use `get_project` alongside `get_logged_time` to trigger alerts when budget hours are exceeded. Your Mastra AI agents can monitor these thresholds in the background. It removes the need for manual check-ins. You write a single workflow that runs on a schedule, checking project health and notifying the team if a task goes off the rails.

Human-in-the-loop task approval

Set `requireToolApproval` on `create_allocation` to ensure a manager reviews every change. Your agents propose the schedule, but you maintain final say. It’s a balanced way to manage resources. You get the speed of automation with the safety of human oversight, preventing accidental double-bookings.

Setup guide

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

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

const result = await agent.generate(
  "List recent Float 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 Float. 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.

Why Choose Vinkius

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place for every integration

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Common questions about Float MCP in Mastra AI

Import the tools into your agent definition and use the workflow engine to define your branching logic. You can easily connect the Float MCP Server to any step in your pipeline.

Yes. You can configure retry policies with exponential backoff directly in your workflow. If an allocation fails due to a network blip, the agent handles it.

Absolutely. You can pass the output of `list_people` into a second tool like `get_person` to drill down into specific resource availability for complex scheduling tasks.

You provide one endpoint token via the Vinkius platform. The server handles the handshake, so your code remains clean and focused on business logic.

Your team lists and project assignments are processed in an ephemeral sandbox. Once the agent task completes, the runtime environment is purged, leaving no persistent traces of your sensitive scheduling data.

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