How to Use the Cradl AI MCP in Mastra AI

Q: How do I add Cradl AI tools to a Mastra AI workflow?

You instantiate the MCP client and spread mcpClient.listTools() into your Mastra agent's tools configuration. From there, your workflow steps can call extractdatafromurl as part of your automated pipeline.

Q: How do I require human approval for Cradl AI tasks in Mastra AI?

You can configure Mastra's requireToolApproval on tools like extractdatafromurl. This pauses the workflow and waits for an admin to approve the document processing step before sending the file to Cradl.

Q: How does the agent know which extraction model to use?

Your Mastra AI agent can run listextractionmodels to see which Cradl AI parsers are currently trained and active. It then picks the best fit based on the document type it's currently analyzing in the workflow.

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Self-healing extraction pipelines in Mastra AI

Document extraction fails when schemas change or files are blurry. By registering this MCP Server in your Mastra AI workflows, you can build conditional branches that catch these failures and retry automatically. If `extract_data_from_url` fails due to a low confidence score, Mastra's workflow engine can automatically route the file to a human reviewer or try an alternative model. You get a resilient pipeline that doesn't crash on bad scans.

Automated routing based on document flows

Let your Mastra AI agent inspect how your documents should move. The agent calls `get_flow_details` to read your active processing rules and maps out the next steps for incoming files. Using `list_workflows`, the agent matches incoming file types to the correct pipeline. It ensures that invoices go to accounting and medical forms go to compliance without hardcoded routing scripts.

Intelligent model fallback systems

When your primary model fails to parse a document, your agent can search for alternatives. Use `search_models_by_name` to find backup models on the fly during workflow execution. The agent then queries `get_model_details` to verify the schema matches before running the backup. This keeping your background jobs running even when your primary parser is undergoing maintenance.

Setup guide

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

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

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

You instantiate the MCP client and spread `mcpClient.listTools()` into your Mastra agent's tools configuration. From there, your workflow steps can call `extract_data_from_url` as part of your automated pipeline.

Yes, Mastra AI has a built-in workflow engine with automatic retries and exponential backoff. If `get_task_status` returns a temporary failure, Mastra will pause and retry the check without losing the document context.

You can configure Mastra's `requireToolApproval` on tools like `extract_data_from_url`. This pauses the workflow and waits for an admin to approve the document processing step before sending the file to Cradl.

Your Mastra AI agent can run `list_extraction_models` to see which Cradl AI parsers are currently trained and active. It then picks the best fit based on the document type it's currently analyzing in the workflow.

Completely. Vinkius executes the MCP Server in an ephemeral, zero-trust container. Your extracted invoice metadata and raw text never persist on the hosting platform, keeping your business data private.

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