How to Use the Assembly MCP in Mastra AI

Q: How does this MCP Server handle large lists of notes?

Tools like listnotes return paginated or structured data directly to the workflow. The agent parses the payload and can loop through individual items automatically.

Build resilient Assembly workflows with Mastra AI. Pull notes, users, and workspaces with built-in retries and conditional branching.

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Connect Assembly MCP to Mastra AI

Create your Vinkius account to connect Assembly 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 Assembly with Mastra AI

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Automate Client Syncs via Mastra AI

Workflows break when APIs timeout. By wrapping this MCP server in your workflow engine, you get automatic exponential backoff when pulling large directories. If a request fails, the system tries again before throwing an error. You can trigger `list_companies` on a schedule and pipe the output directly into downstream systems. Use `get_client` to fetch specific details, knowing the transport layer handles connection drops automatically.

Conditionally Route Workspace Data

Hardcoded scripts fail when edge cases appear. Your agent can read a workspace status using `get_workspace` and decide what to do next. If the space is active, it might pull all related communications via `list_notes`. The framework parses the JSON response and evaluates your branching logic. You define the conditions in TypeScript, and the agent executes the correct path based on the actual Assembly records.

Audit Team Members Safely

Sometimes you need human oversight before touching sensitive directories. You can configure `list_users` and `get_user` with the requireToolApproval flag. The workflow pauses, asks for confirmation, and only proceeds when you say so. Running these audits through this MCP server means the execution happens in a zero-trust environment. You pass the server URL into your Mastra configuration, and the agent handles the rest.

Setup guide

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

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

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

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Common questions about Assembly MCP in Mastra AI

Install @mastra/mcp@latest and initialize a new MCPClient. Pass your Vinkius URL into the servers object. Then call listTools() and spread them into your agent's tool array.

Yes. The framework includes built-in exponential backoff. If a `get_workspace` call drops, the engine automatically attempts the request again based on your configured retry limits.

Tools like `list_notes` return paginated or structured data directly to the workflow. The agent parses the payload and can loop through individual items automatically.

The framework auto-detects whether your Vinkius endpoint uses Streamable HTTP or SSE. You just supply the URL, and the client establishes the correct connection.

Every tool call runs inside an isolated V8 sandbox that dies the second the task completes. Your workflow pulls the text using `get_note`, and Vinkius leaves no trace of the content on our servers.

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