How to Use the Harness MCP in Mastra AI

Q: Can Mastra AI harness Harness for automated retries?

Absolutely. Use the workflow engine to poll getexecutionstatus and trigger a new executepipeline if the previous one hits a non-transient error.

Q: How do I harness Harness environment lists for safety?

Set up a check in your agent logic that calls listenvironments before any deployment. This prevents the agent from deploying to production by accident.

Q: Does it support human-in-the-loop approvals?

Yes. Use the requireToolApproval flag on sensitive tools like executepipeline. This forces the agent to ask you before it changes anything.

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Setup Harness with Mastra AI

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Automated pipeline recovery in Mastra AI

Configure your agents to watch for failures using `get_execution_status`. If a build breaks, the agent automatically triggers a rollback via `execute_pipeline`. You define the logic for when to retry or when to alert an engineer. It removes the manual grind of checking logs every time a deployment hits a snag.

Multi-step workflow orchestration

Chain your infrastructure tasks together using the Harness MCP server. Use `list_services` to find the target and `list_environments` to ensure you are hitting the right cluster. Your agent handles the sequence. It verifies the environment before it fires, preventing the kind of mistakes that happen when people are rushing a fix.

Secure secret management for agents

Access your environment secrets programmatically without hardcoding values. The agent calls `list_secrets` to identify what it needs for the current task. This keeps your configurations dynamic. You can rotate keys or update environment variables in Harness, and the agent picks up the changes on the next run.

Setup guide

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

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

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

Register the server in your Mastra client configuration. Once added, the agent treats your pipelines and services as native capabilities.

Absolutely. Use the workflow engine to poll `get_execution_status` and trigger a new `execute_pipeline` if the previous one hits a non-transient error.

Set up a check in your agent logic that calls `list_environments` before any deployment. This prevents the agent from deploying to production by accident.

Yes. Use the `requireToolApproval` flag on sensitive tools like `execute_pipeline`. This forces the agent to ask you before it changes anything.

The server uses your endpoint token to talk to the Harness API. No credentials are stored on our end; they live in your secure environment variables.

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