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How to integrate Parallel MCP with Mastra AI

Connect Mastra AI to Parallel MCP. Find top articles on generative ai trends, summarize recent news about electric vehicles, and more using natural language, with authentication handled for you.

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Parallel

Api Key

Parallel is a Task API for automated, structured web research and data extraction. It transforms natural language queries into precise, schema-driven outputs for streamlined workflows.

42 Tools

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Introduction

This guide walks you through connecting Parallel to Mastra AI using the Composio tool router. By the end, you'll have a working Parallel agent that can run research tasks, search and extract web content, find matching entities, set up monitors, and generate grounded answers through natural language commands.

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

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

For exact tool slugs and parameter tables, see the tool catalog in the Composio docs.

Also integrate Parallel with

Claude Code Claude Cowork OpenAI Agents SDK Claude Agent SDK Codex OpenClaw Hermes Atomic Agent Google ADK LangChain Vercel AI SDK LlamaIndex CrewAI Pydantic AI AutoGen

TL;DR

Here's what you'll learn:

Set up your environment so Mastra, OpenAI, and Composio work together
Create a Tool Router session in Composio that exposes Parallel tools
Connect Mastra's MCP client to the Composio generated MCP URL
Fetch Parallel tool definitions and attach them as a toolset
Build a Mastra agent that can reason, call tools, and return structured results
Run an interactive CLI where you can chat with your Parallel agent

What is Mastra AI?

Mastra AI is a TypeScript framework for building AI agents with tool support. It provides a clean API for creating agents that can use external services through MCP.

Key features include:

MCP Client: Built-in support for Model Context Protocol servers
Toolsets: Organize tools into logical groups
Step Callbacks: Monitor and debug agent execution
OpenAI Integration: Works with OpenAI models via @ai-sdk/openai

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

The Parallel MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Parallel account. It provides structured and secure access to advanced web research automation, so your agent can perform actions like launching batch research tasks, running semantic searches, monitoring task progress, and generating research suggestions on your behalf.

Automated web research task creation: Instantly create structured research tasks or batch multiple queries for parallel execution, saving time and effort.
Semantic search across multiple topics: Direct your agent to run parallel semantic searches and retrieve top-matching documents or data for several queries at once.
Real-time task group monitoring: Let your agent stream live updates about the progress, completion, or status of ongoing research task groups.
Context-driven research suggestions: Have the agent suggest the next best research tasks based on your project or intent, keeping your workflow efficient and on track.
Task group retrieval and management: Fetch detailed information about specific research task groups to review results or track progress seamlessly.

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 step09 STEPS

Prerequisites

Before starting, make sure you have:

Node.js 18 or higher
A Composio account with an active API key
An OpenAI API key
Basic familiarity with TypeScript

Getting API Keys for OpenAI and Composio

OpenAI API Key

Go to the OpenAI dashboard and create an API key.
You need credits or a connected billing setup to use the models.
Store the key somewhere safe.

Composio API Key

Log in to the Composio dashboard.
Go to Settings and copy your API key.
This key lets your Mastra agent talk to Composio and reach Parallel through MCP.

Install dependencies

bash

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

Install the required packages.

What's happening:

@composio/core is the Composio SDK for creating MCP sessions
@mastra/core provides the Agent class
@mastra/mcp is Mastra's MCP client
@ai-sdk/openai is the model wrapper for OpenAI
dotenv loads environment variables from .env

Set up environment variables

bash

COMPOSIO_API_KEY=your_composio_api_key_here
COMPOSIO_USER_ID=your_user_id_here
OPENAI_API_KEY=your_openai_api_key_here

Create a .env file in your project root.

What's happening:

COMPOSIO_API_KEY authenticates your requests to Composio
COMPOSIO_USER_ID tells Composio which user this session belongs to
OPENAI_API_KEY lets the Mastra agent call OpenAI models

Import libraries and validate environment

typescript

import "dotenv/config";
import { openai } from "@ai-sdk/openai";
import { Agent } from "@mastra/core/agent";
import { MCPClient } from "@mastra/mcp";
import { Composio } from "@composio/core";
import * as readline from "readline";

import type { AiMessageType } from "@mastra/core/agent";

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

if (!openaiAPIKey) 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 as string,
});

What's happening:

dotenv/config auto loads your .env so process.env.* is available
openai gives you a Mastra compatible model wrapper
Agent is the Mastra agent that will call tools and produce answers
MCPClient connects Mastra to your Composio MCP server
Composio is used to create a Tool Router session

Create a Tool Router session for Parallel

typescript

async function main() {
  const session = await composio.create(
    composioUserID as string,
    {
      toolkits: ["parallel"],
    },
  );

  const composioMCPUrl = session.mcp.url;
  console.log("Parallel MCP URL:", composioMCPUrl);

What's happening:

create spins up a short-lived MCP HTTP endpoint for this user
The toolkits array contains "parallel" for Parallel access
session.mcp.url is the MCP URL that Mastra's MCPClient will connect to

Configure Mastra MCP client and fetch tools

typescript

const mcpClient = new MCPClient({
    id: composioUserID as string,
    servers: {
      nasdaq: {
        url: new URL(composioMCPUrl),
        requestInit: {
          headers: session.mcp.headers,
        },
      },
    },
    timeout: 30_000,
  });

console.log("Fetching MCP tools from Composio...");
const composioTools = await mcpClient.getTools();
console.log("Number of tools:", Object.keys(composioTools).length);

What's happening:

MCPClient takes an id for this client and a list of MCP servers
The headers property includes the x-api-key for authentication
getTools fetches the tool definitions exposed by the Parallel toolkit

Create the Mastra agent

typescript

const agent = new Agent({
    name: "parallel-mastra-agent",
    instructions: "You are an AI agent with Parallel tools via Composio.",
    model: "openai/gpt-5",
  });

What's happening:

Agent is the core Mastra agent
name is just an identifier for logging and debugging
instructions guide the agent to use tools instead of only answering in natural language
model uses openai("gpt-5") to configure the underlying LLM

Set up interactive chat interface

typescript

let messages: AiMessageType[] = [];

console.log("Chat started! Type 'exit' or 'quit' to end.\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({
    id: crypto.randomUUID(),
    role: "user",
    content: trimmedInput,
  });

  console.log("\nAgent is thinking...\n");

  try {
    const response = await agent.generate(messages, {
      toolsets: {
        parallel: composioTools,
      },
      maxSteps: 8,
    });

    const { text } = response;

    if (text && text.trim().length > 0) {
      console.log(`Agent: ${text}\n`);
        messages.push({
          id: crypto.randomUUID(),
          role: "assistant",
          content: text,
        });
      }
    } catch (error) {
      console.error("\nError:", error);
    }

    rl.prompt();
  });

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

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

What's happening:

messages keeps the full conversation history in Mastra's expected format
agent.generate runs the agent with conversation history and Parallel toolsets
maxSteps limits how many tool calls the agent can take in a single run
onStepFinish is a hook that prints intermediate steps for debugging

Complete Code

Here's the complete code to get you started with Parallel and Mastra AI:

typescript

import "dotenv/config";
import { openai } from "@ai-sdk/openai";
import { Agent } from "@mastra/core/agent";
import { MCPClient } from "@mastra/mcp";
import { Composio } from "@composio/core";
import * as readline from "readline";

import type { AiMessageType } from "@mastra/core/agent";

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

if (!openaiAPIKey) 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 as string });

async function main() {
  const session = await composio.create(composioUserID as string, {
    toolkits: ["parallel"],
  });

  const composioMCPUrl = session.mcp.url;

  const mcpClient = new MCPClient({
    id: composioUserID as string,
    servers: {
      parallel: {
        url: new URL(composioMCPUrl),
        requestInit: {
          headers: session.mcp.headers,
        },
      },
    },
    timeout: 30_000,
  });

  const composioTools = await mcpClient.getTools();

  const agent = new Agent({
    name: "parallel-mastra-agent",
    instructions: "You are an AI agent with Parallel tools via Composio.",
    model: "openai/gpt-5",
  });

  let messages: AiMessageType[] = [];

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

  rl.prompt();

  rl.on("line", async (input: string) => {
    const trimmed = input.trim();
    if (["exit", "quit"].includes(trimmed.toLowerCase())) {
      rl.close();
      return;
    }

    messages.push({ id: crypto.randomUUID(), role: "user", content: trimmed });

    const { text } = await agent.generate(messages, {
      toolsets: { parallel: composioTools },
      maxSteps: 8,
    });

    if (text) {
      console.log(`Agent: ${text}\n`);
      messages.push({ id: crypto.randomUUID(), role: "assistant", content: text });
    }

    rl.prompt();
  });

  rl.on("close", async () => {
    await mcpClient.disconnect();
    process.exit(0);
  });
}

main();

Conclusion

You've built a Mastra AI agent that can interact with Parallel through Composio's Tool Router. You can extend this further by:

Adding other toolkits like Gmail, Slack, or GitHub
Building a web-based chat interface around this agent
Using multiple MCP endpoints to enable cross-app workflows

TOOLS

Supported Tools

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

Add Enrichment to FindAll Run

Tool to add an enrichment to a FindAll run.

Add Runs to Task Group

Tool to initiate multiple task runs within a TaskGroup.

Cancel FindAll Run

Tool to cancel an active FindAll run by findall_id.

Create Chat Completions

Tool to get realtime chat completions from Parallel AI.

Create Monitor

Tool to create a web monitor that periodically runs the specified query.

Create Task Group

Tool to create a new task group.

Create Task Run

Tool to create and initiate a task run.

Delete Monitor

Tool to delete a monitor, stopping all future executions.

Extend FindAll Run

Tool to extend a FindAll run by adding additional matches to the current match limit.

Extract Content from URLs

Tool to extract relevant content from specific web URLs.

Extract Web Content

Extract excerpts or full content from known web URLs with Parallel's current stable Extract API.

Fetch Task Group Runs

Fetch Task Group run events from a resumable stream, optionally filtering by status and including inputs and outputs.

Start FindAll Run

Tool to start a FindAll run.

Generate Grounded Response

Generate a synchronous answer grounded in live web research with URL citation annotations.

Get FindAll Run Result

Tool to fetch the final (or latest available) FindAll candidates and result payload for a run.

Get FindAll Run Schema

Tool to retrieve the schema configuration of a FindAll run by findall_id.

Get Monitor

Retrieve a monitor's configuration and runtime state by ID using Parallel's current stable Monitor API.

Get Task Group

Retrieve a current stable-API snapshot of a Task Group's aggregate run counts and status.

Ingest FindAll Run

Tool to transform a natural language search objective into a structured FindAll specification.

List Current Monitors

List monitors from Parallel's current stable Monitor API, newest first, with optional type and status filters.

List Monitor Events

Tool to list events for a monitor from up to the last 300 event groups.

List Monitors

List monitors with their configuration and current state.

List Task Group Runs

Collect Task Group runs from the current stable API's resumable event stream, optionally including structured inputs and outputs.

List Task Run Events

Collect progress and state events from a Task Run using the current stable API.

Make Decisions

Answer 1-100 named predicate, choice, or score questions from shared state using current web evidence.

Retrieve Event Group

Tool to retrieve an event group for a monitor.

Retrieve FindAll Run Status

Tool to retrieve status and metadata for a FindAll run by findall_id.

Retrieve Memory

Retrieve relevant memories ranked against a query, or recent memories when the query is empty or omitted.

Retrieve Monitor

Tool to retrieve a specific monitor by ID.

Retrieve Task Group

Tool to retrieve details of a specific task group.

Retrieve Task Group Run

Tool to retrieve run status by run_id for a task group.

Retrieve Task Run

Tool to retrieve run status by run_id.

Retrieve Task Run Input

Tool to retrieve the input data of a specific task run by run_id.

Retrieve Task Run Result

Tool to retrieve the result of a task run by run_id, blocking until the run completes.

Parallel Search

Tool to perform parallel semantic search.

Search Web

Search the web with Parallel's current stable Search API and return ranked results with markdown excerpts.

Simulate Event

Tool to simulate sending an event for a monitor.

Stream FindAll Events

Collect FindAll run events, including candidate discovery, matching progress and run status, and return them as a list after the event stream ends.

Stream Task Group Events

Collect Task Group events, including group status and task run updates, and return them as a list after the event stream ends.

Stream Task Run Events

Collect Task Run events, including progress updates and state changes, and return them as a list after the event stream ends.

Suggest Task

Tool to suggest tasks based on user intent.

Update Monitor

Tool to update a monitor's configuration.

FRAMEWORKS

How to build Parallel MCP Agent with another framework

Claude Code

Use Parallel MCP with Claude Code

Claude Cowork

Use Parallel MCP with Claude Cowork

OpenAI Agents SDK

Use Parallel MCP with OpenAI Agents SDK

Claude Agent SDK

Use Parallel MCP with Claude Agent SDK

Codex

Use Parallel MCP with Codex

OpenClaw

Use Parallel MCP with OpenClaw

Hermes

Use Parallel MCP with Hermes

Atomic Agent

Use Parallel MCP with Atomic Agent

Google ADK

Use Parallel MCP with Google ADK

LangChain

Use Parallel MCP with LangChain

Vercel AI SDK

Use Parallel MCP with Vercel AI SDK

LlamaIndex

Use Parallel MCP with LlamaIndex

CrewAI

Use Parallel MCP with CrewAI

Pydantic AI

Use Parallel MCP with Pydantic AI

AutoGen

Use Parallel MCP with AutoGen

MORE TOOLKITS

Explore Other Toolkits

Toolkit marketplace

Firecrawl

Api Key

Firecrawl automates large-scale web crawling and data extraction. It helps organizations efficiently gather, index, and analyze content from online sources.

Tavily

Api Key

Tavily offers powerful search and data retrieval from documents, databases, and the web. It helps teams locate and filter information instantly, saving hours on research.

Exa

Api Key

Exa is a data extraction and search platform for gathering and analyzing information from websites, APIs, or databases. It helps teams quickly surface insights and automate data-driven workflows.

Serpapi

Api Key

SerpApi is a real-time API for structured search engine results. It lets you automate SERP data collection, parsing, and analysis for SEO and research.

FAQ

Frequently asked questions

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

Yes, you can. Mastra AI 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 Parallel tools.

Yes, absolutely. You can configure which Parallel 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 Parallel data and credentials are handled as safely as possible.

Start with Parallel.It takes 30 seconds.

Managed auth, hosted MCP servers, and every Parallel tool your agent needs.Free to start.

Start building

How to integrate Parallel MCP with Mastra AI

Connect Parallel without auth hassles

Introduction

Also integrate Parallel with

TL;DR

What is Mastra AI?

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

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

What is Composio SDK?

How the Composio SDK works

Step-by-step Guide

Prerequisites

Getting API Keys for OpenAI and Composio

Install dependencies

Set up environment variables

Import libraries and validate environment

Create a Tool Router session for Parallel

Configure Mastra MCP client and fetch tools

Create the Mastra agent

Set up interactive chat interface

Complete Code

Conclusion

Supported Tools

How to build Parallel MCP Agent with another framework

Claude Code

Claude Cowork

OpenAI Agents SDK

Claude Agent SDK

Codex

OpenClaw

Hermes

Atomic Agent

Google ADK

LangChain

Vercel AI SDK

LlamaIndex

CrewAI

Pydantic AI

AutoGen

Explore Other Toolkits

Firecrawl

Tavily

Exa

Serpapi

Frequently asked questions

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

Can I use Tool Router MCP with Mastra AI?

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

How safe is my data with Composio Tool Router?

Start with Parallel.It takes 30 seconds.