How to Use the BlazeMeter MCP in Mastra AI

Build resilient performance testing workflows with BlazeMeter and Mastra AI.

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Connect BlazeMeter MCP to Mastra AI

Create your Vinkius account to connect BlazeMeter 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 BlazeMeter with Mastra AI

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Automate BlazeMeter test cycles

Chain your performance tasks using Mastra's workflow engine. Call `start_test` as the first step, then use `get_master` to verify the gateway status before proceeding. If the gateway reports an error, you can build a branch that triggers a retry or notifies your team. This keeps your testing pipeline moving without manual oversight.

Inspect BlazeMeter test configuration

Pull your test environment details using `get_test` to ensure your parameters match your production requirements. It provides a reliable way to check vault limits before kicking off a load job. Use `list_tests` to gather all active configurations in a workspace. This allows your agents to choose the right test based on the specific project needs.

Manage BlazeMeter workspaces programmatically

Organize your testing environment by querying `list_workspaces` through your agent. It helps your automated systems stay aware of where every project sits. Keep your resource usage tight by using `stop_master` when a test finishes. Your agents can monitor these states and clean up resources automatically.

Setup guide

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

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

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

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Common questions about BlazeMeter MCP in Mastra AI

Add the server to your agent definition. You can then define a workflow that executes `start_test` followed by conditional checks on the output.

It handles the token pass-through via the MCP client settings. You just provide the endpoint and the agent manages the connection.

The server exposes 10 tools that your agent can invoke. You simply list the tools in the agent configuration to give it access to your performance data.

Yes, you can define a retry policy in your Mastra workflow. If `start_test` fails, the engine can trigger a second attempt automatically.

All data regarding your performance metrics is encrypted in transit. We ensure that only your authorized agent workflow accesses your specific test arrays.

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