How to integrate Parallel MCP with Google ADK

This guide walks you through connecting Parallel to Google ADK using the Composio tool router. By the end, you'll have a working Parallel agent that can find top articles on generative ai trends, summarize recent news about electric vehicles, batch search for competitors' product launches through natural language commands. This guide will help you understand how to give your Google ADK 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.

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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.

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Introduction

This guide walks you through connecting Parallel to Google ADK using the Composio tool router. By the end, you'll have a working Parallel agent that can find top articles on generative ai trends, summarize recent news about electric vehicles, batch search for competitors' product launches through natural language commands.

This guide will help you understand how to give your Google ADK 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.

Also integrate Parallel with

OpenAI Agents SDKClaude Agent SDKClaude CodeClaude CoworkCodexOpenClawHermesCLILangChainVercel AI SDKMastra AILlamaIndexCrewAI

TL;DR

Here's what you'll learn:
  • Get a Parallel account set up and connected to Composio
  • Install the Google ADK and Composio packages
  • Create a Composio Tool Router session for Parallel
  • Build an agent that connects to Parallel through MCP
  • Interact with Parallel using natural language

What is Google ADK?

Google ADK (Agents Development Kit) is Google's framework for building AI agents powered by Gemini models. It provides tools for creating agents that can use external services through the Model Context Protocol.

Key features include:

  • Gemini Integration: Native support for Google's Gemini models
  • MCP Toolset: Built-in support for Model Context Protocol tools
  • Streamable HTTP: Connect to external services through streamable HTTP
  • CLI and Web UI: Run agents via command line or web interface

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:

  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

Step by step09 STEPS
1

Prerequisites

Before starting, make sure you have:
  • A Google API key for Gemini models
  • A Composio account and API key
  • Python 3.9 or later installed
  • Basic familiarity with Python
2

Getting API Keys for Google and Composio

Google API Key
  • Go to Google AI Studio and create an API key.
  • Copy the key and keep it safe. You will put this in GOOGLE_API_KEY.
Composio API Key and User ID
  • Log in to the Composio dashboard.
  • Go to Settings → API Keys and copy your Composio API key. Use this for COMPOSIO_API_KEY.
  • Decide on a stable user identifier to scope sessions, often your email or a user ID. Use this for COMPOSIO_USER_ID.
3

Install dependencies

bash
pip install google-adk composio python-dotenv

Inside your virtual environment, install the required packages.

What's happening:

  • google-adk is Google's Agents Development Kit
  • composio connects your agent to Parallel via MCP
  • python-dotenv loads environment variables
4

Set up ADK project

bash
adk create my_agent

Set up a new Google ADK project.

What's happening:

  • This creates an agent folder with a root agent file and .env file
5

Set environment variables

bash
GOOGLE_API_KEY=your-google-api-key
COMPOSIO_API_KEY=your-composio-api-key
COMPOSIO_USER_ID=your-user-id-or-email

Save all your credentials in the .env file.

What's happening:

  • GOOGLE_API_KEY authenticates with Google's Gemini models
  • COMPOSIO_API_KEY authenticates with Composio
  • COMPOSIO_USER_ID identifies the user for session management
6

Import modules and validate environment

python
import os
import warnings

from composio import Composio
from dotenv import load_dotenv
from google.adk.agents.llm_agent import Agent
from google.adk.tools.mcp_tool.mcp_session_manager import StreamableHTTPConnectionParams
from google.adk.tools.mcp_tool.mcp_toolset import McpToolset

load_dotenv()

warnings.filterwarnings("ignore", message=".*BaseAuthenticatedTool.*")

GOOGLE_API_KEY = os.getenv("GOOGLE_API_KEY")
COMPOSIO_API_KEY = os.getenv("COMPOSIO_API_KEY")
COMPOSIO_USER_ID = os.getenv("COMPOSIO_USER_ID")

if not GOOGLE_API_KEY:
    raise ValueError("GOOGLE_API_KEY is not set in the environment.")
if not COMPOSIO_API_KEY:
    raise ValueError("COMPOSIO_API_KEY is not set in the environment.")
if not COMPOSIO_USER_ID:
    raise ValueError("COMPOSIO_USER_ID is not set in the environment.")
What's happening:
  • os reads environment variables
  • Composio is the main Composio SDK client
  • GoogleProvider declares that you are using Google ADK as the agent runtime
  • Agent is the Google ADK LLM agent class
  • McpToolset lets the ADK agent call MCP tools over HTTP
7

Create Composio client and Tool Router session

python
composio_client = Composio(api_key=COMPOSIO_API_KEY)

composio_session = composio_client.create(
    user_id=COMPOSIO_USER_ID,
    toolkits=["parallel"],
)

COMPOSIO_MCP_URL = composio_session.mcp.url,
print(f"Composio MCP URL: {COMPOSIO_MCP_URL}")
What's happening:
  • Authenticates to Composio with your API key
  • Declares Google ADK as the provider
  • Spins up a short-lived MCP endpoint for your user and selected toolkit
  • Stores the MCP HTTP URL for the ADK MCP integration
8

Set up the McpToolset and create the Agent

python
composio_toolset = McpToolset(
    connection_params=StreamableHTTPConnectionParams(
        url=COMPOSIO_MCP_URL,
        headers={"x-api-key": COMPOSIO_API_KEY}
    )
)

root_agent = Agent(
    model="gemini-2.5-flash",
    name="composio_agent",
    description="An agent that uses Composio tools to perform actions.",
    instruction=(
        "You are a helpful assistant connected to Composio. "
        "You have the following tools available: "
        "COMPOSIO_SEARCH_TOOLS, COMPOSIO_MULTI_EXECUTE_TOOL, "
        "COMPOSIO_MANAGE_CONNECTIONS, COMPOSIO_REMOTE_BASH_TOOL, COMPOSIO_REMOTE_WORKBENCH. "
        "Use these tools to help users with Parallel operations."
    ),
    tools=[composio_toolset],
)

print("\nAgent setup complete. You can now run this agent directly ;)")
What's happening:
  • Connects the ADK agent to the Composio MCP endpoint through McpToolset
  • Uses Gemini as the model powering the agent
  • Lists exact tool names in instruction to reduce misnamed tool calls
9

Run the agent

bash
# Run in CLI mode
adk run my_agent

# Or run in web UI mode
adk web

Execute the agent from the project root. The web command opens a web portal where you can chat with the agent.

What's happening:

  • adk run runs the agent in CLI mode
  • adk web . opens a web UI for interactive testing

Complete Code

Here's the complete code to get you started with Parallel and Google ADK:

python
import os
import warnings

from composio import Composio
from composio_google import GoogleProvider
from dotenv import load_dotenv
from google.adk.agents.llm_agent import Agent
from google.adk.tools.mcp_tool.mcp_session_manager import StreamableHTTPConnectionParams
from google.adk.tools.mcp_tool.mcp_toolset import McpToolset

load_dotenv()
warnings.filterwarnings("ignore", message=".*BaseAuthenticatedTool.*")

GOOGLE_API_KEY = os.getenv("GOOGLE_API_KEY")
COMPOSIO_API_KEY = os.getenv("COMPOSIO_API_KEY")
COMPOSIO_USER_ID = os.getenv("COMPOSIO_USER_ID")

if not GOOGLE_API_KEY:
    raise ValueError("GOOGLE_API_KEY is not set in the environment.")
if not COMPOSIO_API_KEY:
    raise ValueError("COMPOSIO_API_KEY is not set in the environment.")
if not COMPOSIO_USER_ID:
    raise ValueError("COMPOSIO_USER_ID is not set in the environment.")

composio_client = Composio(api_key=COMPOSIO_API_KEY, provider=GoogleProvider())

composio_session = composio_client.create(
    user_id=COMPOSIO_USER_ID,
    toolkits=["parallel"],
)

COMPOSIO_MCP_URL = composio_session.mcp.url


composio_toolset = McpToolset(
    connection_params=StreamableHTTPConnectionParams(
        url=COMPOSIO_MCP_URL,
        headers={"x-api-key": COMPOSIO_API_KEY}
    )
)

root_agent = Agent(
    model="gemini-2.5-flash",
    name="composio_agent",
    description="An agent that uses Composio tools to perform actions.",
    instruction=(
        "You are a helpful assistant connected to Composio. "
        "You have the following tools available: "
        "COMPOSIO_SEARCH_TOOLS, COMPOSIO_MULTI_EXECUTE_TOOL, "
        "COMPOSIO_MANAGE_CONNECTIONS, COMPOSIO_REMOTE_BASH_TOOL, COMPOSIO_REMOTE_WORKBENCH. "
        "Use these tools to help users with Parallel operations."
    ),  
    tools=[composio_toolset],
)

print("\nAgent setup complete. You can now run this agent directly ;)")

Conclusion

You've successfully integrated Parallel with the Google ADK through Composio's MCP Tool Router. Your agent can now interact with Parallel using natural language commands.

Key takeaways:

  • The Tool Router approach dynamically routes requests to the appropriate Parallel tools
  • Environment variables keep your credentials secure and separate from code
  • Clear agent instructions reduce tool calling errors
  • The ADK web UI provides an interactive interface for testing and development

You can extend this setup by adding more toolkits to the toolkits array in your session configuration.

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.

Fetch Task Group Runs

Tool to retrieve task runs from a Task Group as a resumable stream.

Start FindAll Run

Tool to start a FindAll run.

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.

Ingest FindAll Run

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

List Monitor Events

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

List Monitors

Tool to list active monitors for the user.

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 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.

Simulate Event

Tool to simulate sending an event for a monitor.

Stream FindAll Events

Tool to stream events from a FindAll run.

Stream Task Group Events

Tool to stream events for a Task Group.

Stream Task Run Events

Tool to stream events for a Task Run.

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

OpenAI Agents SDK logo

OpenAI Agents SDK

Use Parallel MCP with OpenAI Agents SDK

Claude Agent SDK logo

Claude Agent SDK

Use Parallel MCP with Claude Agent SDK

Claude Code logo

Claude Code

Use Parallel MCP with Claude Code

Claude Cowork logo

Claude Cowork

Use Parallel MCP with Claude Cowork

Codex logo

Codex

Use Parallel MCP with Codex

OpenClaw logo

OpenClaw

Use Parallel MCP with OpenClaw

Hermes logo

Hermes

Use Parallel MCP with Hermes

CLI logo

CLI

Use Parallel MCP with CLI

LangChain logo

LangChain

Use Parallel MCP with LangChain

Vercel AI SDK logo

Vercel AI SDK

Use Parallel MCP with Vercel AI SDK

Mastra AI logo

Mastra AI

Use Parallel MCP with Mastra AI

LlamaIndex logo

LlamaIndex

Use Parallel MCP with LlamaIndex

CrewAI logo

CrewAI

Use Parallel MCP with CrewAI

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. Google ADK 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.

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