How to integrate Blazemeter MCP with LlamaIndex

This guide walks you through connecting Blazemeter to LlamaIndex 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 LlamaIndex 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.

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Introduction

This guide will help you understand how to give your LlamaIndex 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

OpenAI Agents SDK Claude Agent SDK Claude Code Claude Cowork Codex OpenClaw Hermes CLI Google ADK LangChain Vercel AI SDK Mastra AI CrewAI

TL;DR

Here's what you'll learn:

Set your OpenAI and Composio API keys
Install LlamaIndex and Composio packages
Create a Composio Tool Router session for Blazemeter
Connect LlamaIndex to the Blazemeter MCP server
Build a Blazemeter-powered agent using LlamaIndex
Interact with Blazemeter through natural language

What is LlamaIndex?

LlamaIndex is a data framework for building LLM applications. It provides tools for connecting LLMs to external data sources and services through agents and tools.

Key features include:

ReAct Agent: Reasoning and acting pattern for tool-using agents
MCP Tools: Native support for Model Context Protocol
Context Management: Maintain conversation context across interactions
Async Support: Built for async/await patterns

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.

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:

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

Step-by-step Guide

Step by step10 STEPS

Prerequisites

Before you begin, make sure you have:

Python 3.8/Node 16 or higher installed
A Composio account with the API key
An OpenAI API key
A Blazemeter account and project
Basic familiarity with async Python/Typescript

Getting API Keys for OpenAI, Composio, and Blazemeter

OpenAI API key (OPENAI_API_KEY)

Go to the OpenAI dashboard
Create an API key if you don't have one
Assign it to OPENAI_API_KEY in .env

Composio API key and user ID

Log into the Composio dashboard
Copy your API key from Settings
- Use this as COMPOSIO_API_KEY
Pick a stable user identifier (email or ID)
- Use this as COMPOSIO_USER_ID

Installing dependencies

npm install @composio/llamaindex @llamaindex/openai @llamaindex/tools @llamaindex/workflow dotenv

Create a new Typescript project and install the necessary dependencies:

@composio/llamaindex: Composio's LlamaIndex integration
@llamaindex/openai: OpenAI LLM integration
@llamaindex/tools: MCP client for LlamaIndex
@llamaindex/workflow: Workflow framework for LlamaIndex
dotenv: Environment variable management

Set environment variables

bash

OPENAI_API_KEY=your-openai-api-key
COMPOSIO_API_KEY=your-composio-api-key
COMPOSIO_USER_ID=your-user-id

Create a .env file in your project root:

These credentials will be used to:

Authenticate with OpenAI's GPT-5 model
Connect to Composio's Tool Router
Identify your Composio user session for Blazemeter access

Import modules

import "dotenv/config";
import readline from "node:readline/promises";
import { stdin as input, stdout as output } from "node:process";

import { Composio } from "@composio/core";

import { mcp } from "@llamaindex/tools";
import { agent as createAgent } from "@llamaindex/workflow";
import { openai } from "@llamaindex/openai";

dotenv.config();

Create a new file called blazemeter_llamaindex_agent.ts and import the required modules:

Key imports:

dotenv.config loads .env at runtime
readline gives us a simple CLI chat loop
Composio is the main Composio SDK client
mcp connects to an MCP endpoint
createAgent builds a LlamaIndex agent
openai configures the LLM backend

Load environment variables and initialize Composio

const OPENAI_API_KEY = process.env.OPENAI_API_KEY;
const COMPOSIO_API_KEY = process.env.COMPOSIO_API_KEY;
const COMPOSIO_USER_ID = process.env.COMPOSIO_USER_ID;

if (!OPENAI_API_KEY) throw new Error("OPENAI_API_KEY is not set");
if (!COMPOSIO_API_KEY) throw new Error("COMPOSIO_API_KEY is not set");
if (!COMPOSIO_USER_ID) throw new Error("COMPOSIO_USER_ID is not set");

What's happening:

This ensures missing credentials cause early, clear errors before the agent attempts to initialise.

Create a Tool Router session and build the agent function

async function buildAgent() {

  console.log(`Initializing Composio client...${COMPOSIO_USER_ID!}...`);
  console.log(`COMPOSIO_USER_ID: ${COMPOSIO_USER_ID!}...`);

  const composio = new Composio({
    apiKey: COMPOSIO_API_KEY,
    provider: new LlamaindexProvider(),
  });

  const session = await composio.create(
    COMPOSIO_USER_ID!,
    {
      toolkits: ["blazemeter"],
    },
  );

  const mcpUrl = session.mcp.url;
  console.log(`Composio Tool Router MCP URL: ${mcpUrl}`);

  const server = mcp({
    url: mcpUrl,
    clientName: "composio_tool_router_with_llamaindex",
    requestInit: {
      headers: {
        "x-api-key": COMPOSIO_API_KEY!,
      },
    },
    // verbose: true,
  });

  const tools = await server.tools();

  const llm = openai({ apiKey: OPENAI_API_KEY, model: "gpt-5" });

  const agent = createAgent({
    name: "composio_tool_router_with_llamaindex",
        description : "An agent that uses Composio Tool Router MCP tools to perform actions.",
    systemPrompt:
      "You are a helpful assistant connected to Composio Tool Router."+
"Use the available tools to answer user queries and perform Blazemeter actions." ,
    llm,
    tools,
  });

  return agent;
}

What's happening here:

We create a Composio client using your API key and configure it with the LlamaIndex provider
We then create a tool router MCP session for your user, specifying the toolkits we want to use (in this case, blazemeter)
The session returns an MCP HTTP endpoint URL that acts as a gateway to all your configured tools
LlamaIndex will connect to this endpoint to dynamically discover and use the available Blazemeter tools.
The MCP tools are mapped to LlamaIndex-compatible tools and plug them into the Agent.

Create an interactive chat loop

async function chatLoop(agent: ReturnType<typeof createAgent>) {
  const rl = readline.createInterface({ input, output });

  console.log("Type 'quit' or 'exit' to stop.");

  while (true) {
    let userInput: string;

    try {
      userInput = (await rl.question("\nYou: ")).trim();
    } catch {
      console.log("\nAgent: Bye!");
      break;
    }

    if (!userInput) {
      continue;
    }

    const lower = userInput.toLowerCase();
    if (lower === "quit" || lower === "exit") {
      console.log("Agent: Bye!");
      break;
    }

    try {
      process.stdout.write("Agent: ");

      const stream = agent.runStream(userInput);
      let finalResult: any = null;

      for await (const event of stream) {
        // The event.data contains the streamed content
        const data: any = event.data;

        // Check for streaming delta content
        if (data?.delta) {
          process.stdout.write(data.delta);
        }

        // Store final result for fallback
        if (data?.result || data?.message) {
          finalResult = data;
        }
      }

      // If no streaming happened, show the final result
      if (finalResult) {
        const answer =
          finalResult.result ??
          finalResult.message?.content ??
          finalResult.message ??
          "";
        if (answer && typeof answer === "string" && !answer.includes("[object")) {
          process.stdout.write(answer);
        }
      }

      console.log(); // New line after streaming completes
    } catch (err: any) {
      console.error("\nAgent error:", err?.message ?? err);
    }
  }

  rl.close();
}

What's happening:

We're creating a direct terminal interface to chat with Blazemeter
The LLM's responses are streamed to the CLI for faster interaction.
The agent uses context to maintain conversation history
The agent processes the request, selects appropriate Blazemeter tools, and returns a result
We extract the answer from the result data structure and display it to the user
You can type 'quit' or 'exit' to stop the chat loop gracefully
Agent responses and any errors are streamed in a clear, readable format

Define the main entry point

async function main() {
  try {
    const agent = await buildAgent();
    await chatLoop(agent);
  } catch (err) {
    console.error("Failed to start agent:", err);
    process.exit(1);
  }
}

main();

What's happening here:

We're orchestrating the entire application flow
The agent gets built with proper error handling
Then we kick off the interactive chat loop so you can start talking to Blazemeter

Run the agent

npx ts-node llamaindex-agent.ts

When prompted, authenticate and authorise your agent with Blazemeter, then start asking questions.

Complete Code

Here's the complete code to get you started with Blazemeter and LlamaIndex:

import "dotenv/config";
import readline from "node:readline/promises";
import { stdin as input, stdout as output } from "node:process";

import { Composio } from "@composio/core";
import { LlamaindexProvider } from "@composio/llamaindex";

import { mcp } from "@llamaindex/tools";
import { agent as createAgent } from "@llamaindex/workflow";
import { openai } from "@llamaindex/openai";

dotenv.config();

const OPENAI_API_KEY = process.env.OPENAI_API_KEY;
const COMPOSIO_API_KEY = process.env.COMPOSIO_API_KEY;
const COMPOSIO_USER_ID = process.env.COMPOSIO_USER_ID;

if (!OPENAI_API_KEY) {
    throw new Error("OPENAI_API_KEY is not set in the environment");
  }
if (!COMPOSIO_API_KEY) {
    throw new Error("COMPOSIO_API_KEY is not set in the environment");
  }
if (!COMPOSIO_USER_ID) {
    throw new Error("COMPOSIO_USER_ID is not set in the environment");
  }

async function buildAgent() {

  console.log(`Initializing Composio client...${COMPOSIO_USER_ID!}...`);
  console.log(`COMPOSIO_USER_ID: ${COMPOSIO_USER_ID!}...`);

  const composio = new Composio({
    apiKey: COMPOSIO_API_KEY,
    provider: new LlamaindexProvider(),
  });

  const session = await composio.create(
    COMPOSIO_USER_ID!,
    {
      toolkits: ["blazemeter"],
    },
  );

  const mcpUrl = session.mcp.url;
  console.log(`Composio Tool Router MCP URL: ${mcpUrl}`);

  const server = mcp({
    url: mcpUrl,
    clientName: "composio_tool_router_with_llamaindex",
    requestInit: {
      headers: {
        "x-api-key": COMPOSIO_API_KEY!,
      },
    },
    // verbose: true,
  });

  const tools = await server.tools();

  const llm = openai({ apiKey: OPENAI_API_KEY, model: "gpt-5" });

  const agent = createAgent({
    name: "composio_tool_router_with_llamaindex",
    description:
      "An agent that uses Composio Tool Router MCP tools to perform actions.",
    systemPrompt:
      "You are a helpful assistant connected to Composio Tool Router."+
"Use the available tools to answer user queries and perform Blazemeter actions." ,
    llm,
    tools,
  });

  return agent;
}

async function chatLoop(agent: ReturnType<typeof createAgent>) {
  const rl = readline.createInterface({ input, output });

  console.log("Type 'quit' or 'exit' to stop.");

  while (true) {
    let userInput: string;

    try {
      userInput = (await rl.question("\nYou: ")).trim();
    } catch {
      console.log("\nAgent: Bye!");
      break;
    }

    if (!userInput) {
      continue;
    }

    const lower = userInput.toLowerCase();
    if (lower === "quit" || lower === "exit") {
      console.log("Agent: Bye!");
      break;
    }

    try {
      process.stdout.write("Agent: ");

      const stream = agent.runStream(userInput);
      let finalResult: any = null;

      for await (const event of stream) {
        // The event.data contains the streamed content
        const data: any = event.data;

        // Check for streaming delta content
        if (data?.delta) {
          process.stdout.write(data.delta);
        }

        // Store final result for fallback
        if (data?.result || data?.message) {
          finalResult = data;
        }
      }

      // If no streaming happened, show the final result
      if (finalResult) {
        const answer =
          finalResult.result ??
          finalResult.message?.content ??
          finalResult.message ??
          "";
        if (answer && typeof answer === "string" && !answer.includes("[object")) {
          process.stdout.write(answer);
        }
      }

      console.log(); // New line after streaming completes
    } catch (err: any) {
      console.error("\nAgent error:", err?.message ?? err);
    }
  }

  rl.close();
}

async function main() {
  try {
    const agent = await buildAgent();
    await chatLoop(agent);
  } catch (err: any) {
    console.error("Failed to start agent:", err?.message ?? err);
    process.exit(1);
  }
}

main();

Conclusion

You've successfully connected Blazemeter to LlamaIndex through Composio's Tool Router MCP layer. Key takeaways:

Tool Router dynamically exposes Blazemeter tools through an MCP endpoint
LlamaIndex's ReActAgent handles reasoning and orchestration; Composio handles integrations
The agent becomes more capable without increasing prompt size
Async Python provides clean, efficient execution of agent workflows

You can easily extend this to other toolkits like Gmail, Notion, Stripe, GitHub, and more by adding them to the toolkits parameter.

TOOLS

Supported Tools

Every Blazemeter action and event your agent gets out of the box.

Convert Transactions

Tool to convert transaction files to BlazeMeter DSL format for service virtualization.

Create API Monitoring Schedule

Tool to create a new schedule for running API monitoring tests.

Create Multi Test

Tool to create a new multi-test within a specified project.

Create Private Location

Tool to create a new private location in BlazeMeter.

Create Private Location Agent

Creates a new agent (server) within a BlazeMeter private location.

Create Project

Creates a new project within a BlazeMeter workspace.

Create Search

Execute a search query against BlazeMeter entities using advanced filtering and sorting.

Create Tag

Creates a new tag in BlazeMeter for organizing and categorizing resources.

Create Test

Tool to create a new single test within a specified project.

Create Workspace Asset

Tool to create an asset in a workspace for test data management.

Create Asset Dependency

Tool to create a dependency relationship between two assets in a BlazeMeter workspace.

Create Workspace Package

Creates a new package within a BlazeMeter workspace.

Create Workspace Transactions

Tool to create transactions in a BlazeMeter workspace for service virtualization.

Delete API Monitoring Schedule

Tool to delete a specific test schedule by its ID.

Delete Private Location Workspace

Tool to remove a workspace from a private location.

Delete Project

Tool to delete a specific project by its ID.

Delete Test File

Tool to delete a file from a test.

Delete Tests

Tool to delete a test by its ID.

Delete Workspace Asset Dependency

Tool to delete a dependency from a workspace's asset repository by its ID.

Delete Workspace Asset

Tool to delete an asset from a workspace in BlazeMeter's Asset Repository.

Delete Workspace Assets Dependencies

Tool to delete asset dependencies by source/target in a workspace.

Delete Workspace Logs

Tool to delete master test execution logs from a BlazeMeter workspace.

Delete Workspace Managers

Tool to remove managers from a workspace.

Delete Workspace Package

Tool to delete a package from a workspace in the BlazeMeter Asset Repository.

Duplicate Test

Tool to duplicate an existing test by its ID.

Export Package

Tool to export a package from BlazeMeter Asset Repository as a zip file.

Export Workspaces Packages

Tool to export multiple packages from a workspace as a zip file.

Generate Test Data from Data Model

Tool to generate test data from a data model in Asset Repository.

Generate Workspace Test Data

Tool to generate synthetic test data on-the-fly without storing in Asset Repository.

Get Accounts

Tool to retrieve a list of accounts associated with the authenticated user.

Get API Monitoring Schedule

Tool to retrieve details of a specific API monitoring schedule by its ID.

Get API Monitoring Schedules

Retrieves a paginated list of API monitoring test schedules.

Get Generator Functions

Tool to retrieve all available test data generator functions from BlazeMeter Test Data API.

Get Generator Seed Lists

Tool to retrieve a list of all available seed lists from BlazeMeter Test Data Management API.

Get Info Health

Tool to retrieve the BlazeMeter service health status.

Get Info Version

Tool to retrieve BlazeMeter service version information.

Get Masters Reports Main Summary

Tool to retrieve request statistics summary for a master test run.

Get Multi Test

Tool to retrieve details of a specific multi-test.

Get Multi Tests

Retrieves a paginated list of multi-tests within a BlazeMeter workspace.

Get Private Locations

Tool to retrieve a list of private locations filtered by account or workspace.

Get Project Details

Tool to retrieve detailed information about a specific project by its ID.

Get Projects

Tool to retrieve a list of projects within a specified workspace.

Get Regions

Tool to retrieve a list of all available test regions for API monitoring.

Get Search Metadata

Retrieve metadata about searchable entities, fields, relationships, and operators in BlazeMeter's search API.

Get Shared Folders

Tool to retrieve a list of shared folders within a specified workspace.

Get Tags

Tool to retrieve a list of all tags from BlazeMeter Mock Services API.

Get Test Details

Tool to retrieve complete details of a specific test by its ID.

Get Tests

Retrieve a list of performance tests filtered by workspace or project.

Get Tests Files

Tool to list all files associated with a test.

Get Test Validations

Tool to retrieve validation results for a specific test by its ID.

Get User

Retrieve the authenticated user's profile information including their default project and preferences.

Get User Active Sessions

Tool to retrieve the list of active test sessions for the authenticated user.

Get User Invites

Tool to retrieve pending invites for the authenticated user.

Get User Projects

Tool to retrieve all projects belonging to the authenticated user.

Get Workspace Details

Tool to retrieve detailed information about a specific workspace by its ID.

Get Workspace Package

Tool to retrieve a specific package by its ID from a workspace in the BlazeMeter Asset Repository.

Get Workspaces

Tool to retrieve a list of workspaces for a specified account.

Get Workspace Assets

Tool to retrieve all data models (assets) in a workspace for Test Data Management.

Get Workspace Asset By ID

Tool to retrieve a specific asset by ID from the Test Data Management Asset Repository.

Get Workspace Asset Data

Tool to retrieve data from a specific asset in a workspace's asset repository.

Get Workspace Assets Dependencies

Tool to retrieve all dependencies for a given workspace with optional filtering criteria.

Get Workspace Asset Dependency

Tool to retrieve a specific dependency by ID from a workspace's asset repository.

Get Asset Dependencies

Tool to retrieve dependencies for a specific asset in a workspace from the BlazeMeter Asset Repository.

Get Workspace Data Model By ID

Tool to retrieve a specific data model by ID from a workspace in Test Data Management.

Get Virtual Service Template by ID

Tool to get virtual service template details from a specific workspace.

Get Workspace Packages

Tool to retrieve packages from a BlazeMeter workspace.

Get Workspace Package Dependencies

Tool to retrieve package dependencies for a specific package in a workspace.

Get Workspace Service Mock Templates

Tool to list virtual service templates available in a workspace.

Get Workspace Transactions

Tool to list transactions for virtual services in a workspace.

Get Workspace Users

Tool to retrieve a list of users within a specified workspace.

Import Workspace Package

Import a package from a ZIP file into a BlazeMeter workspace.

List Generator Card Issuers

Tool to retrieve a list of available card issuers for test data generation.

Publish API Data

Publishes test data through the BlazeMeter Test Data Management API.

Tool to register a new user account in BlazeMeter.

Start Test

Tool to start a preconfigured performance load test.

Stop Master

Gracefully stop a running BlazeMeter test execution (master) by its ID.

Stop Test

Tool to stop all active masters (test executions) for a given test ID.

Terminate User Active Sessions

Tool to immediately terminate active user sessions in BlazeMeter.

Terminate Workspaces Masters

Tool to terminate all running masters in a BlazeMeter workspace.

Update API Monitoring Schedule

Tool to update the configuration of an existing API monitoring schedule.

Update Project

Tool to update an existing BlazeMeter project by its ID.

Update Test

Tool to update details of a specific test by its ID.

Update Workspace Asset

Tool to update an existing asset in a BlazeMeter workspace.

Update Workspaces Assets Dependencies

Tool to update asset dependencies in a BlazeMeter workspace.

Update Workspace Package

Tool to update an existing package in a BlazeMeter workspace.

Update Workspace Package Dependencies

Tool to update package dependencies for a specific package in a workspace.

Update Workspace Service Mock Template

Tool to update a virtual service template configuration (Service Virtualization).

Update Workspace User

Tool to update a user's role and status within a BlazeMeter workspace.

Upload Test Files

Upload a file asset (script, data file, or configuration) to a BlazeMeter test.

Upload Workspace Asset Data

Tool to upload asset data to a BlazeMeter workspace.

Validate Test

Tool to validate a specific test by its ID.

Validate Workspace Asset

Tool to validate a data model asset in a workspace for test data management.

FRAMEWORKS

How to build Blazemeter MCP Agent with another framework

OpenAI Agents SDK

Use Blazemeter MCP with OpenAI Agents SDK

Claude Agent SDK

Use Blazemeter MCP with Claude Agent SDK

Claude Code

Use Blazemeter MCP with Claude Code

Claude Cowork

Use Blazemeter MCP with Claude Cowork

Codex

Use Blazemeter MCP with Codex

OpenClaw

Use Blazemeter MCP with OpenClaw

Hermes

Use Blazemeter MCP with Hermes

CLI

Use Blazemeter MCP with CLI

Google ADK

Use Blazemeter MCP with Google ADK

LangChain

Use Blazemeter MCP with LangChain

Vercel AI SDK

Use Blazemeter MCP with Vercel AI SDK

Mastra AI

Use Blazemeter MCP with Mastra AI

CrewAI

Use Blazemeter MCP with CrewAI

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GitHub

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Ably

Api Key

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FAQ

Frequently asked questions

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.

Yes, you can. LlamaIndex 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.

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.

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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