How to integrate Blazemeter MCP with Vercel AI SDK v6

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Introduction

This guide walks you through connecting Blazemeter to Vercel AI SDK v6 using the Composio tool router. By the end, you'll have a working Blazemeter agent that can start a new performance test on your main project, fetch results from the latest test run, list all test runs for project alpha through natural language commands.

This guide will help you understand how to give your Vercel AI SDK agent real control over a Blazemeter account through Composio's Blazemeter MCP server.

Before we dive in, let's take a quick look at the key ideas and tools involved.

Also integrate Blazemeter with

TL;DR

Here's what you'll learn:
  • How to set up and configure a Vercel AI SDK agent with Blazemeter integration
  • Using Composio's Tool Router to dynamically load and access Blazemeter tools
  • Creating an MCP client connection using HTTP transport
  • Building an interactive CLI chat interface with conversation history management
  • Handling tool calls and results within the Vercel AI SDK framework

What is Vercel AI SDK?

The Vercel AI SDK is a TypeScript library for building AI-powered applications. It provides tools for creating agents that can use external services and maintain conversation state.

Key features include:

  • streamText: Core function for streaming responses with real-time tool support
  • MCP Client: Built-in support for Model Context Protocol via @ai-sdk/mcp
  • Step Counting: Control multi-step tool execution with stopWhen: stepCountIs()
  • OpenAI Provider: Native integration with OpenAI models

What is the Blazemeter MCP server, and what's possible with it?

The Blazemeter MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Blazemeter account. It provides structured and secure access so your agent can perform Blazemeter operations on your behalf.

Supported Tools & Triggers

Tools
Convert TransactionsTool to convert transaction files to BlazeMeter DSL format for service virtualization.
Create API Monitoring ScheduleTool to create a new schedule for running API monitoring tests.
Create Multi TestTool to create a new multi-test within a specified project.
Create Private LocationTool to create a new private location in BlazeMeter.
Create Private Location AgentCreates a new agent (server) within a BlazeMeter private location.
Create ProjectCreates a new project within a BlazeMeter workspace.
Create SearchExecute a search query against BlazeMeter entities using advanced filtering and sorting.
Create TagCreates a new tag in BlazeMeter for organizing and categorizing resources.
Create TestTool to create a new single test within a specified project.
Create Workspace AssetTool to create an asset in a workspace for test data management.
Create Asset DependencyTool to create a dependency relationship between two assets in a BlazeMeter workspace.
Create Workspace PackageCreates a new package within a BlazeMeter workspace.
Create Workspace TransactionsTool to create transactions in a BlazeMeter workspace for service virtualization.
Delete API Monitoring ScheduleTool to delete a specific test schedule by its ID.
Delete Private Location WorkspaceTool to remove a workspace from a private location.
Delete ProjectTool to delete a specific project by its ID.
Delete Test FileTool to delete a file from a test.
Delete TestsTool to delete a test by its ID.
Delete Workspace Asset DependencyTool to delete a dependency from a workspace's asset repository by its ID.
Delete Workspace AssetTool to delete an asset from a workspace in BlazeMeter's Asset Repository.
Delete Workspace Assets DependenciesTool to delete asset dependencies by source/target in a workspace.
Delete Workspace LogsTool to delete master test execution logs from a BlazeMeter workspace.
Delete Workspace ManagersTool to remove managers from a workspace.
Delete Workspace PackageTool to delete a package from a workspace in the BlazeMeter Asset Repository.
Duplicate TestTool to duplicate an existing test by its ID.
Export PackageTool to export a package from BlazeMeter Asset Repository as a zip file.
Export Workspaces PackagesTool to export multiple packages from a workspace as a zip file.
Generate Test Data from Data ModelTool to generate test data from a data model in Asset Repository.
Generate Workspace Test DataTool to generate synthetic test data on-the-fly without storing in Asset Repository.
Get AccountsTool to retrieve a list of accounts associated with the authenticated user.
Get API Monitoring ScheduleTool to retrieve details of a specific API monitoring schedule by its ID.
Get API Monitoring SchedulesRetrieves a paginated list of API monitoring test schedules.
Get Generator FunctionsTool to retrieve all available test data generator functions from BlazeMeter Test Data API.
Get Generator Seed ListsTool to retrieve a list of all available seed lists from BlazeMeter Test Data Management API.
Get Info HealthTool to retrieve the BlazeMeter service health status.
Get Info VersionTool to retrieve BlazeMeter service version information.
Get Masters Reports Main SummaryTool to retrieve request statistics summary for a master test run.
Get Multi TestTool to retrieve details of a specific multi-test.
Get Multi TestsRetrieves a paginated list of multi-tests within a BlazeMeter workspace.
Get Private LocationsTool to retrieve a list of private locations filtered by account or workspace.
Get Project DetailsTool to retrieve detailed information about a specific project by its ID.
Get ProjectsTool to retrieve a list of projects within a specified workspace.
Get RegionsTool to retrieve a list of all available test regions for API monitoring.
Get Search MetadataRetrieve metadata about searchable entities, fields, relationships, and operators in BlazeMeter's search API.
Get Shared FoldersTool to retrieve a list of shared folders within a specified workspace.
Get TagsTool to retrieve a list of all tags from BlazeMeter Mock Services API.
Get Test DetailsTool to retrieve complete details of a specific test by its ID.
Get TestsRetrieve a list of performance tests filtered by workspace or project.
Get Tests FilesTool to list all files associated with a test.
Get Test ValidationsTool to retrieve validation results for a specific test by its ID.
Get UserRetrieve the authenticated user's profile information including their default project and preferences.
Get User Active SessionsTool to retrieve the list of active test sessions for the authenticated user.
Get User InvitesTool to retrieve pending invites for the authenticated user.
Get User ProjectsTool to retrieve all projects belonging to the authenticated user.
Get Workspace DetailsTool to retrieve detailed information about a specific workspace by its ID.
Get Workspace PackageTool to retrieve a specific package by its ID from a workspace in the BlazeMeter Asset Repository.
Get WorkspacesTool to retrieve a list of workspaces for a specified account.
Get Workspace AssetsTool to retrieve all data models (assets) in a workspace for Test Data Management.
Get Workspace Asset By IDTool to retrieve a specific asset by ID from the Test Data Management Asset Repository.
Get Workspace Asset DataTool to retrieve data from a specific asset in a workspace's asset repository.
Get Workspace Assets DependenciesTool to retrieve all dependencies for a given workspace with optional filtering criteria.
Get Workspace Asset DependencyTool to retrieve a specific dependency by ID from a workspace's asset repository.
Get Asset DependenciesTool to retrieve dependencies for a specific asset in a workspace from the BlazeMeter Asset Repository.
Get Workspace Data Model By IDTool to retrieve a specific data model by ID from a workspace in Test Data Management.
Get Virtual Service Template by IDTool to get virtual service template details from a specific workspace.
Get Workspace PackagesTool to retrieve packages from a BlazeMeter workspace.
Get Workspace Package DependenciesTool to retrieve package dependencies for a specific package in a workspace.
Get Workspace Service Mock TemplatesTool to list virtual service templates available in a workspace.
Get Workspace TransactionsTool to list transactions for virtual services in a workspace.
Get Workspace UsersTool to retrieve a list of users within a specified workspace.
Import Workspace PackageImport a package from a ZIP file into a BlazeMeter workspace.
List Generator Card IssuersTool to retrieve a list of available card issuers for test data generation.
Publish API DataPublishes test data through the BlazeMeter Test Data Management API.
Register UserTool to register a new user account in BlazeMeter.
Start TestTool to start a preconfigured performance load test.
Stop MasterGracefully stop a running BlazeMeter test execution (master) by its ID.
Stop TestTool to stop all active masters (test executions) for a given test ID.
Terminate User Active SessionsTool to immediately terminate active user sessions in BlazeMeter.
Terminate Workspaces MastersTool to terminate all running masters in a BlazeMeter workspace.
Update API Monitoring ScheduleTool to update the configuration of an existing API monitoring schedule.
Update ProjectTool to update an existing BlazeMeter project by its ID.
Update TestTool to update details of a specific test by its ID.
Update Workspace AssetTool to update an existing asset in a BlazeMeter workspace.
Update Workspaces Assets DependenciesTool to update asset dependencies in a BlazeMeter workspace.
Update Workspace PackageTool to update an existing package in a BlazeMeter workspace.
Update Workspace Package DependenciesTool to update package dependencies for a specific package in a workspace.
Update Workspace Service Mock TemplateTool to update a virtual service template configuration (Service Virtualization).
Update Workspace UserTool to update a user's role and status within a BlazeMeter workspace.
Upload Test FilesUpload a file asset (script, data file, or configuration) to a BlazeMeter test.
Upload Workspace Asset DataTool to upload asset data to a BlazeMeter workspace.
Validate TestTool to validate a specific test by its ID.
Validate Workspace AssetTool to validate a data model asset in a workspace for test data management.

What is the Composio tool router, and how does it fit here?

What is Composio SDK?

Composio's Composio SDK helps agents find the right tools for a task at runtime. You can plug in multiple toolkits (like Gmail, HubSpot, and GitHub), and the agent will identify the relevant app and action to complete multi-step workflows. This can reduce token usage and improve the reliability of tool calls. Read more here: Getting started with Composio SDK

The tool router generates a secure MCP URL that your agents can access to perform actions.

How the Composio SDK works

The Composio SDK follows a three-phase workflow:

  1. Discovery: Searches for tools matching your task and returns relevant toolkits with their details.
  2. Authentication: Checks for active connections. If missing, creates an auth config and returns a connection URL via Auth Link.
  3. Execution: Executes the action using the authenticated connection.

Step-by-step Guide

Prerequisites

Before you begin, make sure you have:
  • Node.js and npm installed
  • A Composio account with API key
  • An OpenAI API key

Getting API Keys for OpenAI and Composio

OpenAI API Key
  • Go to the OpenAI dashboard and create an API key. You'll need credits to use the models, or you can connect to another model provider.
  • Keep the API key safe.
Composio API Key
  • Log in to the Composio dashboard.
  • Navigate to your API settings and generate a new API key.
  • Store this key securely as you'll need it for authentication.

Install required dependencies

bash
npm install @ai-sdk/openai @ai-sdk/mcp @composio/core ai dotenv

First, install the necessary packages for your project.

What you're installing:

  • @ai-sdk/openai: Vercel AI SDK's OpenAI provider
  • @ai-sdk/mcp: MCP client for Vercel AI SDK
  • @composio/core: Composio SDK for tool integration
  • ai: Core Vercel AI SDK
  • dotenv: Environment variable management

Set up environment variables

bash
OPENAI_API_KEY=your_openai_api_key_here
COMPOSIO_API_KEY=your_composio_api_key_here
COMPOSIO_USER_ID=your_user_id_here

Create a .env file in your project root.

What's needed:

  • OPENAI_API_KEY: Your OpenAI API key for GPT model access
  • COMPOSIO_API_KEY: Your Composio API key for tool access
  • COMPOSIO_USER_ID: A unique identifier for the user session

Import required modules and validate environment

typescript
import "dotenv/config";
import { openai } from "@ai-sdk/openai";
import { Composio } from "@composio/core";
import * as readline from "readline";
import { streamText, type ModelMessage, stepCountIs } from "ai";
import { createMCPClient } from "@ai-sdk/mcp";

const composioAPIKey = process.env.COMPOSIO_API_KEY;
const composioUserID = process.env.COMPOSIO_USER_ID;

if (!process.env.OPENAI_API_KEY) throw new Error("OPENAI_API_KEY is not set");
if (!composioAPIKey) throw new Error("COMPOSIO_API_KEY is not set");
if (!composioUserID) throw new Error("COMPOSIO_USER_ID is not set");

const composio = new Composio({
  apiKey: composioAPIKey,
});
What's happening:
  • We're importing all necessary libraries including Vercel AI SDK's OpenAI provider and Composio
  • The dotenv/config import automatically loads environment variables
  • The MCP client import enables connection to Composio's tool server

Create Tool Router session and initialize MCP client

typescript
async function main() {
  // Create a tool router session for the user
  const session = await composio.create(composioUserID!, {
    toolkits: ["blazemeter"],
  });

  const mcpUrl = session.mcp.url;
What's happening:
  • We're creating a Tool Router session that gives your agent access to Blazemeter tools
  • The create method takes the user ID and specifies which toolkits should be available
  • The returned mcp object contains the URL and authentication headers needed to connect to the MCP server
  • This session provides access to all Blazemeter-related tools through the MCP protocol

Connect to MCP server and retrieve tools

typescript
const mcpClient = await createMCPClient({
  transport: {
    type: "http",
    url: mcpUrl,
    headers: session.mcp.headers, // Authentication headers for the Composio MCP server
  },
});

const tools = await mcpClient.tools();
What's happening:
  • We're creating an MCP client that connects to our Composio Tool Router session via HTTP
  • The mcp.url provides the endpoint, and mcp.headers contains authentication credentials
  • The type: "http" is important - Composio requires HTTP transport
  • tools() retrieves all available Blazemeter tools that the agent can use

Initialize conversation and CLI interface

typescript
let messages: ModelMessage[] = [];

console.log("Chat started! Type 'exit' or 'quit' to end the conversation.\n");
console.log(
  "Ask any questions related to blazemeter, like summarize my last 5 emails, send an email, etc... :)))\n",
);

const rl = readline.createInterface({
  input: process.stdin,
  output: process.stdout,
  prompt: "> ",
});

rl.prompt();
What's happening:
  • We initialize an empty messages array to maintain conversation history
  • A readline interface is created to accept user input from the command line
  • Instructions are displayed to guide the user on how to interact with the agent

Handle user input and stream responses with real-time tool feedback

typescript
rl.on("line", async (userInput: string) => {
  const trimmedInput = userInput.trim();

  if (["exit", "quit", "bye"].includes(trimmedInput.toLowerCase())) {
    console.log("\nGoodbye!");
    rl.close();
    process.exit(0);
  }

  if (!trimmedInput) {
    rl.prompt();
    return;
  }

  messages.push({ role: "user", content: trimmedInput });
  console.log("\nAgent is thinking...\n");

  try {
    const stream = streamText({
      model: openai("gpt-5"),
      messages,
      tools,
      toolChoice: "auto",
      stopWhen: stepCountIs(10),
      onStepFinish: (step) => {
        for (const toolCall of step.toolCalls) {
          console.log(`[Using tool: ${toolCall.toolName}]`);
          }
          if (step.toolCalls.length > 0) {
            console.log(""); // Add space after tool calls
          }
        },
      });

      for await (const chunk of stream.textStream) {
        process.stdout.write(chunk);
      }

      console.log("\n\n---\n");

      // Get final result for message history
      const response = await stream.response;
      if (response?.messages?.length) {
        messages.push(...response.messages);
      }
    } catch (error) {
      console.error("\nAn error occurred while talking to the agent:");
      console.error(error);
      console.log(
        "\nYou can try again or restart the app if it keeps happening.\n",
      );
    } finally {
      rl.prompt();
    }
  });

  rl.on("close", async () => {
    await mcpClient.close();
    console.log("\n👋 Session ended.");
    process.exit(0);
  });
}

main().catch((err) => {
  console.error("Fatal error:", err);
  process.exit(1);
});
What's happening:
  • We use streamText instead of generateText to stream responses in real-time
  • toolChoice: "auto" allows the model to decide when to use Blazemeter tools
  • stopWhen: stepCountIs(10) allows up to 10 steps for complex multi-tool operations
  • onStepFinish callback displays which tools are being used in real-time
  • We iterate through the text stream to create a typewriter effect as the agent responds
  • The complete response is added to conversation history to maintain context
  • Errors are caught and displayed with helpful retry suggestions

Complete Code

Here's the complete code to get you started with Blazemeter and Vercel AI SDK:

typescript
import "dotenv/config";
import { openai } from "@ai-sdk/openai";
import { Composio } from "@composio/core";
import * as readline from "readline";
import { streamText, type ModelMessage, stepCountIs } from "ai";
import { createMCPClient } from "@ai-sdk/mcp";

const composioAPIKey = process.env.COMPOSIO_API_KEY;
const composioUserID = process.env.COMPOSIO_USER_ID;

if (!process.env.OPENAI_API_KEY) throw new Error("OPENAI_API_KEY is not set");
if (!composioAPIKey) throw new Error("COMPOSIO_API_KEY is not set");
if (!composioUserID) throw new Error("COMPOSIO_USER_ID is not set");

const composio = new Composio({
  apiKey: composioAPIKey,
});

async function main() {
  // Create a tool router session for the user
  const session = await composio.create(composioUserID!, {
    toolkits: ["blazemeter"],
  });

  const mcpUrl = session.mcp.url;

  const mcpClient = await createMCPClient({
    transport: {
      type: "http",
      url: mcpUrl,
      headers: session.mcp.headers, // Authentication headers for the Composio MCP server
    },
  });

  const tools = await mcpClient.tools();

  let messages: ModelMessage[] = [];

  console.log("Chat started! Type 'exit' or 'quit' to end the conversation.\n");
  console.log(
    "Ask any questions related to blazemeter, like summarize my last 5 emails, send an email, etc... :)))\n",
  );

  const rl = readline.createInterface({
    input: process.stdin,
    output: process.stdout,
    prompt: "> ",
  });

  rl.prompt();

  rl.on("line", async (userInput: string) => {
    const trimmedInput = userInput.trim();

    if (["exit", "quit", "bye"].includes(trimmedInput.toLowerCase())) {
      console.log("\nGoodbye!");
      rl.close();
      process.exit(0);
    }

    if (!trimmedInput) {
      rl.prompt();
      return;
    }

    messages.push({ role: "user", content: trimmedInput });
    console.log("\nAgent is thinking...\n");

    try {
      const stream = streamText({
        model: openai("gpt-5"),
        messages,
        tools,
        toolChoice: "auto",
        stopWhen: stepCountIs(10),
        onStepFinish: (step) => {
          for (const toolCall of step.toolCalls) {
            console.log(`[Using tool: ${toolCall.toolName}]`);
          }
          if (step.toolCalls.length > 0) {
            console.log(""); // Add space after tool calls
          }
        },
      });

      for await (const chunk of stream.textStream) {
        process.stdout.write(chunk);
      }

      console.log("\n\n---\n");

      // Get final result for message history
      const response = await stream.response;
      if (response?.messages?.length) {
        messages.push(...response.messages);
      }
    } catch (error) {
      console.error("\nAn error occurred while talking to the agent:");
      console.error(error);
      console.log(
        "\nYou can try again or restart the app if it keeps happening.\n",
      );
    } finally {
      rl.prompt();
    }
  });

  rl.on("close", async () => {
    await mcpClient.close();
    console.log("\n👋 Session ended.");
    process.exit(0);
  });
}

main().catch((err) => {
  console.error("Fatal error:", err);
  process.exit(1);
});

Conclusion

You've successfully built a Blazemeter agent using the Vercel AI SDK with streaming capabilities! This implementation provides a powerful foundation for building AI applications with natural language interfaces and real-time feedback.

Key features of this implementation:

  • Real-time streaming responses for a better user experience with typewriter effect
  • Live tool execution feedback showing which tools are being used as the agent works
  • Dynamic tool loading through Composio's Tool Router with secure authentication
  • Multi-step tool execution with configurable step limits (up to 10 steps)
  • Comprehensive error handling for robust agent execution
  • Conversation history maintenance for context-aware responses

You can extend this further by adding custom error handling, implementing specific business logic, or integrating additional Composio toolkits to create multi-app workflows.

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FAQ

What are the differences in Tool Router MCP and Blazemeter MCP?

With a standalone Blazemeter MCP server, the agents and LLMs can only access a fixed set of Blazemeter tools tied to that server. However, with the Composio Tool Router, agents can dynamically load tools from Blazemeter and many other apps based on the task at hand, all through a single MCP endpoint.

Can I use Tool Router MCP with Vercel AI SDK v6?

Yes, you can. Vercel AI SDK v6 fully supports MCP integration. You get structured tool calling, message history handling, and model orchestration while Tool Router takes care of discovering and serving the right Blazemeter tools.

Can I manage the permissions and scopes for Blazemeter while using Tool Router?

Yes, absolutely. You can configure which Blazemeter scopes and actions are allowed when connecting your account to Composio. You can also bring your own OAuth credentials or API configuration so you keep full control over what the agent can do.

How safe is my data with Composio Tool Router?

All sensitive data such as tokens, keys, and configuration is fully encrypted at rest and in transit. Composio is SOC 2 Type 2 compliant and follows strict security practices so your Blazemeter data and credentials are handled as safely as possible.

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