How to Use the Tower MCP in Mastra AI

Q: Can I reliably list projects in Tower using Mastra AI?

Absolutely. You can set up a workflow that calls listprojects. If the initial connection fails, Mastra AI's built-in retry logic kicks in, giving you a solid chance of retrieving all project names.

Q: If I update a task in Tower with Mastra AI, what if it fails?

Mastra AI is designed for this. If the updatetask call fails (maybe due to permissions), your workflow can automatically back off and retry the operation later.

Q: What type of data does Tower touch when using Mastra AI?

This server touches highly sensitive user records, including team member details, project ownership, and task assignments. The framework manages these calls securely across multiple steps.

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Connect Tower MCP to Mastra AI

Create your Vinkius account to connect Tower 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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Perplexity

Complex Workflow Management via MCP Server

You can build multi-step processes that handle failures. For example, a workflow might first use `list_projects` to find the scope, and if that fails, it retries using a different lookup mechanism. This resilience is key. Mastra AI makes sure your agent doesn't just crash when an external tool call like one from Tower hiccups; it handles the failure gracefully.

Automated Task Creation and Updates

Need to create a task based on project scope? Your workflow calls `create_task`. If that initial attempt fails because of bad parameters, Mastra AI can automatically retry with different inputs. The process is robust. It runs the full loop: read project details via `get_project`, then attempt creation via `create_task`, all while monitoring for failure and retrying.

Project Scope Discovery

Building a system that needs to know what's going on? Your workflow can reliably call `list_tasks` across multiple projects. Mastra AI manages the sequence of calls needed to cover all bases. It guarantees that you check every project listed via `list_projects`, ensuring your subsequent logic has complete visibility into the scope.

Setup guide

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

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

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

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Common questions about Tower MCP in Mastra AI

Absolutely. You can set up a workflow that calls `list_projects`. If the initial connection fails, Mastra AI's built-in retry logic kicks in, giving you a solid chance of retrieving all project names.

The MCP Server exposes 10 tools. You simply expose the necessary ones—like `get_task_details` and `list_members`—to your workflow engine. The framework handles the execution order.

It handles key project data, including task status and team membership details via tools like `get_task_details` and `list_members`. This allows your workflow to make decisions based on current user roles.

Mastra AI is designed for this. If the `update_task` call fails (maybe due to permissions), your workflow can automatically back off and retry the operation later.

This server touches highly sensitive `user records`, including team member details, project ownership, and task assignments. The framework manages these calls securely across multiple steps.

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