BlazeMeter MCP Server for Vercel AI SDK 10 tools — connect in under 2 minutes
The Vercel AI SDK is the TypeScript toolkit for building AI-powered applications. Connect BlazeMeter through Vinkius and every tool is available as a typed function. ready for React Server Components, API routes, or any Node.js backend.
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Vinkius supports streamable HTTP and SSE.
import { createMCPClient } from "@ai-sdk/mcp";
import { generateText } from "ai";
import { openai } from "@ai-sdk/openai";
async function main() {
const mcpClient = await createMCPClient({
transport: {
type: "http",
// Your Vinkius token. get it at cloud.vinkius.com
url: "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp",
},
});
try {
const tools = await mcpClient.tools();
const { text } = await generateText({
model: openai("gpt-4o"),
tools,
prompt: "Using BlazeMeter, list all available capabilities.",
});
console.log(text);
} finally {
await mcpClient.close();
}
}
main();
* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure
About BlazeMeter MCP Server
Connect your BlazeMeter API credentials to any AI agent and integrate enterprise load testing natively into your DevOps and QA workflows.
The Vercel AI SDK gives every BlazeMeter tool full TypeScript type inference, IDE autocomplete, and compile-time error checking. Connect 10 tools through Vinkius and stream results progressively to React, Svelte, or Vue components. works on Edge Functions, Cloudflare Workers, and any Node.js runtime.
What you can do
- Infrastructure Management — List thoroughly your bounded Workspaces, Projects, and structural user metadata.
- Test Operations — Discover configured JMeter definitions and dynamically start active cloud-based performance hosts to execute load scaling securely.
- Live Run Monitoring — Query the operational health of live "Master" runs, fetch precise throughput reports (p90/p99 KPIs), and monitor active limits.
- Emergency Controls — Forcefully shut down runaway active cloud connections to protect source architecture during testing.
The BlazeMeter MCP Server exposes 10 tools through the Vinkius. Connect it to Vercel AI SDK in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.
How to Connect BlazeMeter to Vercel AI SDK via MCP
Follow these steps to integrate the BlazeMeter MCP Server with Vercel AI SDK.
Install dependencies
Run npm install @ai-sdk/mcp ai @ai-sdk/openai
Replace the token
Replace [YOUR_TOKEN_HERE] with your Vinkius token
Run the script
Save to agent.ts and run with npx tsx agent.ts
Explore tools
The SDK discovers 10 tools from BlazeMeter and passes them to the LLM
Why Use Vercel AI SDK with the BlazeMeter MCP Server
Vercel AI SDK provides unique advantages when paired with BlazeMeter through the Model Context Protocol.
TypeScript-first: every MCP tool gets full type inference, IDE autocomplete, and compile-time error checking out of the box
Framework-agnostic core works with Next.js, Nuxt, SvelteKit, or any Node.js runtime. same BlazeMeter integration everywhere
Built-in streaming UI primitives let you display BlazeMeter tool results progressively in React, Svelte, or Vue components
Edge-compatible: the AI SDK runs on Vercel Edge Functions, Cloudflare Workers, and other edge runtimes for minimal latency
BlazeMeter + Vercel AI SDK Use Cases
Practical scenarios where Vercel AI SDK combined with the BlazeMeter MCP Server delivers measurable value.
AI-powered web apps: build dashboards that query BlazeMeter in real-time and stream results to the UI with zero loading states
API backends: create serverless endpoints that orchestrate BlazeMeter tools and return structured JSON responses to any frontend
Chatbots with tool use: embed BlazeMeter capabilities into conversational interfaces with streaming responses and tool call visibility
Internal tools: build admin panels where team members interact with BlazeMeter through natural language queries
BlazeMeter MCP Tools for Vercel AI SDK (10)
These 10 tools become available when you connect BlazeMeter to Vercel AI SDK via MCP:
get_master
Dispatch an automated validation check routing explicit Gateway run status
get_report
Inspect deep internal arrays mitigating specific Plan Math Reports
get_test
Retrieve explicit configuration tracing an active Vault limit Test
get_user
Identify precise active arrays spanning native Identity parsing
list_masters
Enumerate explicitly attached structured rules exporting active Master records
list_projects
Perform structural extraction of Projects bounded to a Workspace
list_tests
Provision a highly-available JSON Payload extracting bound Tests
list_workspaces
Identify bounded Workspace records inside the Headless BlazeMeter Platform
start_test
Irreversibly execute explicit load generation validations spanning rich metrics
stop_master
Identify precise active arrays spanning native Gateway shutdown logic
Example Prompts for BlazeMeter in Vercel AI SDK
Ready-to-use prompts you can give your Vercel AI SDK agent to start working with BlazeMeter immediately.
"List the performance testing projects inside Workspace ID `123456`."
"Trigger a new execution for load Test ID `987654`."
"Stop the actively running Master test ID `m-11223` immediately."
Troubleshooting BlazeMeter MCP Server with Vercel AI SDK
Common issues when connecting BlazeMeter to Vercel AI SDK through the Vinkius, and how to resolve them.
createMCPClient is not a function
npm install @ai-sdk/mcpBlazeMeter + Vercel AI SDK FAQ
Common questions about integrating BlazeMeter MCP Server with Vercel AI SDK.
How does the Vercel AI SDK connect to MCP servers?
createMCPClient from @ai-sdk/mcp and pass the server URL. The SDK discovers all tools and provides typed TypeScript interfaces for each one.Can I use MCP tools in Edge Functions?
Does it support streaming tool results?
useChat and streamText that handle tool calls and display results progressively in the UI.Connect BlazeMeter with your favorite client
Step-by-step setup guides for every MCP-compatible client and framework:
Anthropic's native desktop app for Claude with built-in MCP support.
AI-first code editor with integrated LLM-powered coding assistance.
GitHub Copilot in VS Code with Agent mode and MCP support.
Purpose-built IDE for agentic AI coding workflows.
Autonomous AI coding agent that runs inside VS Code.
Anthropic's agentic CLI for terminal-first development.
Python SDK for building production-grade OpenAI agent workflows.
Google's framework for building production AI agents.
Type-safe agent development for Python with first-class MCP support.
TypeScript toolkit for building AI-powered web applications.
TypeScript-native agent framework for modern web stacks.
Python framework for orchestrating collaborative AI agent crews.
Leading Python framework for composable LLM applications.
Data-aware AI agent framework for structured and unstructured sources.
Microsoft's framework for multi-agent collaborative conversations.
Connect BlazeMeter to Vercel AI SDK
Get your token, paste the configuration, and start using 10 tools in under 2 minutes. No API key management needed.