How to Use the Clustdoc MCP in Mastra AI

Build automated document intake flows in Mastra AI using this MCP Server.

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Connect Clustdoc MCP to Mastra AI

Create your Vinkius account to connect Clustdoc 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 Clustdoc with Mastra AI

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Automate Clustdoc workflows in Mastra AI

Chain your intake processes together using the `list_workflows` tool. You can build logic that triggers specific actions if a client fails to upload a document on time. This MCP Server plugs into your agent's toolset, allowing it to read your entire onboarding structure. Your agents can now make decisions based on live submission data.

Handle Clustdoc application status with Mastra AI

Use `get_application_details` to fetch deep insights on any specific onboarding file. If the data shows a missing signature, your agent can automatically ping the client. Your Mastra agent manages the retry logic internally. It checks the status, identifies the gap, and updates the application state without manual intervention.

Monitor Clustdoc webhooks via Mastra AI

Track incoming events by running `list_webhooks`. Your agents stay aware of every status change in your intake funnel as it happens. When a webhook fires, the agent can cross-reference the data with `list_applications`. This loop keeps your onboarding operations running without you needing to check the dashboard.

Setup guide

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

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

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

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Common questions about Clustdoc MCP in Mastra AI

Add the server URL to your agent configuration. The framework will automatically register the tools so your agent can call them during its execution path.

You can define retry logic in your workflow definitions. If a tool call fails, the framework attempts the operation again based on your specified backoff settings.

Yes, you can set tool approval requirements. The agent will pause and wait for your signal before it finalizes any document intake changes.

Certainly. You can share the toolset across different agents, letting one focus on research and another on managing the document collection process.

Access is restricted to the specific API tokens you provide. The server processes your document metadata in a locked sandbox, ensuring no other agents see your intake files.

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