How to Use the Treblle MCP in Mastra AI

Q: How can Treblle help me manage complex workflows with Mastra AI?

Treblle's ingestapidata tool gives your agent real-time API observability. This allows the workflow engine to make smart, informed decisions based on actual traffic data, improving overall process reliability.

Q: Is Treblle good for investigating workflow failures with Mastra AI?

It's perfect. You can use ingestapidata to get a historical view of both the request and response that caused the failure, letting your agent pinpoint exactly why the process broke.

Build resilient workflows with Treblle's reliable data context for Mastra AI.

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Connect Treblle MCP to Mastra AI

Create your Vinkius account to connect Treblle 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 Treblle with Mastra AI

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Contextual Data for Workflow Decisions

The `ingest_api_data` tool provides your agent with a clear view of API traffic. This means complex workflows can use real-time observability metrics to make decisions, like deciding whether to retry a transaction or alert an admin. For Mastra AI's built-in workflow engine, having clean data context is everything. You know exactly what the failure was—the request and response that caused it.

Secure Failure Analysis

When a multi-step process fails, you need to debug without risking PII exposure. `ingest_api_data` masks sensitive fields like passwords and SSNs automatically. You can analyze the failure point—the exact request/response pair—without compromising security. This lets your agent understand *why* a conditional branch was triggered or why an automatic retry failed.

Monitoring API Calls for Retries

Need to build workflows with exponential backoff and retries? The `ingest_api_data` tool monitors the underlying API calls. You get instant documentation of both the initial attempt's request and the subsequent response. This structured data helps your agent analyze performance metrics, allowing it to decide if a retry is warranted or if a different payment method should be used.

Setup guide

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

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

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

Treblle's `ingest_api_data` tool gives your agent real-time API observability. This allows the workflow engine to make smart, informed decisions based on actual traffic data, improving overall process reliability.

Yes. The server automatically masks highly sensitive fields like passwords, CC numbers, and SSNs before transmission. This ensures you can debug workflow failures securely without exposing private user data.

It handles full API request and response data. Specifically, it monitors and documents fields like passwords, CCs, and SSNs, ensuring your agent has the necessary context to proceed.

It's perfect. You can use `ingest_api_data` to get a historical view of both the request and response that caused the failure, letting your agent pinpoint exactly why the process broke.

It touches raw API traffic data. This includes structured details about requests and responses, while rigorously masking sensitive types such as SSNs and passwords.

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