How to integrate Zenserp MCP with OpenAI Agents SDK

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Introduction

This guide walks you through connecting Zenserp to the OpenAI Agents SDK using the Composio tool router. By the end, you'll have a working Zenserp agent that can find top news articles on ai ethics, get trending keywords for electric cars, list local coffee shops in brooklyn, search product prices for wireless earbuds through natural language commands.

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

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

TL;DR

Here's what you'll learn:
  • Get and set up your OpenAI and Composio API keys
  • Install the necessary dependencies
  • Initialize Composio and create a Tool Router session for Zenserp
  • Configure an AI agent that can use Zenserp as a tool
  • Run a live chat session where you can ask the agent to perform Zenserp operations

What is open-ai-agents-sdk?

The OpenAI Agents SDK is a lightweight framework for building AI agents that can use tools and maintain conversation state. It provides a simple interface for creating agents with hosted MCP tool support.

Key features include:

  • Hosted MCP Tools: Connect to external services through hosted MCP endpoints
  • SQLite Sessions: Persist conversation history across interactions
  • Simple API: Clean interface with Agent, Runner, and tool configuration
  • Streaming Support: Real-time response streaming for interactive applications

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

The Zenserp MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Zenserp account. It provides structured and secure access to real-time search engine results, so your agent can perform actions like running Google searches, grabbing news headlines, pulling images, analyzing trends, and even fetching local business data on your behalf.

  • Comprehensive Google and Bing search: Instantly run structured web searches and retrieve up-to-date SERP data from Google or Bing for any query.
  • Automated news and trend analysis: Have your agent fetch recent Google News articles or analyze keyword popularity over time using Google Trends data.
  • Reverse image and visual content search: Perform reverse image lookups or image searches to discover where an image appears online or find relevant pictures for any topic.
  • Shopping and video discovery: Search Google Shopping for product offers or Google Video for relevant multimedia results, all via agent-driven queries.
  • Local and map-based business lookup: Let your agent use Google Maps search to find businesses or places based on location, keywords, or coordinates for local intelligence.

Supported Tools & Triggers

Tools
Bing SearchTool to obtain bing search results.
Google News SearchTool to perform a google news search.
Google Reverse Image SearchTool to perform a reverse image search on google.
Google Shopping SearchTool to perform a google shopping search.
Google TrendsTool to retrieve google trends data.
Google Video SearchTool to perform a google video search via zenserp.
Yandex Search via ZenserpTool to obtain yandex search results via zenserp api.
Zenserp Google Image SearchTool to perform a google image search via zenserp.
Google Maps SearchTool to perform a google maps (local) search.
Zenserp Google SearchTool to perform a standard google search via zenserp.
Google Shopping SearchTool to perform a google shopping search.

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

What is Tool Router?

Composio's Tool Router 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 Tool Router

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

How the Tool Router works

The Tool Router 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 starting, make sure you have:
  • Composio API Key and OpenAI API Key
  • Primary know-how of OpenAI Agents SDK
  • A live Zenserp project
  • Some knowledge of Python or Typescript

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

Install dependencies

pip install composio_openai_agents openai-agents python-dotenv

Install the Composio SDK and the OpenAI Agents SDK.

Set up environment variables

bash
OPENAI_API_KEY=sk-...your-api-key
COMPOSIO_API_KEY=your-api-key
USER_ID=composio_user@gmail.com

Create a .env file and add your OpenAI and Composio API keys.

Import dependencies

import asyncio
import os
from dotenv import load_dotenv

from composio import Composio
from composio_openai_agents import OpenAIAgentsProvider
from agents import Agent, Runner, HostedMCPTool, SQLiteSession
What's happening:
  • You're importing all necessary libraries.
  • The Composio and OpenAIAgentsProvider classes are imported to connect your OpenAI agent to Composio tools like Zenserp.

Set up the Composio instance

load_dotenv()

api_key = os.getenv("COMPOSIO_API_KEY")
user_id = os.getenv("USER_ID")

if not api_key:
    raise RuntimeError("COMPOSIO_API_KEY is not set. Create a .env file with COMPOSIO_API_KEY=your_key")

# Initialize Composio
composio = Composio(api_key=api_key, provider=OpenAIAgentsProvider())
What's happening:
  • load_dotenv() loads your .env file so OPENAI_API_KEY and COMPOSIO_API_KEY are available as environment variables.
  • Creating a Composio instance using the API Key and OpenAIAgentsProvider class.

Create a Tool Router session

# Create a Zenserp Tool Router session
session = composio.create(
    user_id=user_id,
    toolkits=["zenserp"]
)

mcp_url = session.mcp.url

What is happening:

  • You give the Tool Router the user id and the toolkits you want available. Here, it is only zenserp.
  • The router checks the user's Zenserp connection and prepares the MCP endpoint.
  • The returned session.mcp.url is the MCP URL that your agent will use to access Zenserp.
  • This approach keeps things lightweight and lets the agent request Zenserp tools only when needed during the conversation.

Configure the agent

# Configure agent with MCP tool
agent = Agent(
    name="Assistant",
    model="gpt-5",
    instructions=(
        "You are a helpful assistant that can access Zenserp. "
        "Help users perform Zenserp operations through natural language."
    ),
    tools=[
        HostedMCPTool(
            tool_config={
                "type": "mcp",
                "server_label": "tool_router",
                "server_url": mcp_url,
                "headers": {"x-api-key": api_key},
                "require_approval": "never",
            }
        )
    ],
)
What's happening:
  • We're creating an Agent instance with a name, model (gpt-5), and clear instructions about its purpose.
  • The agent's instructions tell it that it can access Zenserp and help with queries, inserts, updates, authentication, and fetching database information.
  • The tools array includes a HostedMCPTool that connects to the MCP server URL we created earlier.
  • The headers dict includes the Composio API key for secure authentication with the MCP server.
  • require_approval: 'never' means the agent can execute Zenserp operations without asking for permission each time, making interactions smoother.

Start chat loop and handle conversation

print("\nComposio Tool Router session created.")

chat_session = SQLiteSession("conversation_openai_toolrouter")

print("\nChat started. Type your requests below.")
print("Commands: 'exit', 'quit', or 'q' to end\n")

async def main():
    try:
        result = await Runner.run(
            agent,
            "What can you help me with?",
            session=chat_session
        )
        print(f"Assistant: {result.final_output}\n")
    except Exception as e:
        print(f"Error: {e}\n")

    while True:
        user_input = input("You: ").strip()
        if user_input.lower() in {"exit", "quit", "q"}:
            print("Goodbye!")
            break

        result = await Runner.run(
            agent,
            user_input,
            session=chat_session
        )
        print(f"Assistant: {result.final_output}\n")

asyncio.run(main())
What's happening:
  • The program prints a session URL that you visit to authorize Zenserp.
  • After authorization, the chat begins.
  • Each message you type is processed by the agent using Runner.run().
  • The responses are printed to the console, and conversations are saved locally using SQLite.
  • Typing exit, quit, or q cleanly ends the chat.

Complete Code

Here's the complete code to get you started with Zenserp and open-ai-agents-sdk:

import asyncio
import os
from dotenv import load_dotenv

from composio import Composio
from composio_openai_agents import OpenAIAgentsProvider
from agents import Agent, Runner, HostedMCPTool, SQLiteSession

load_dotenv()

api_key = os.getenv("COMPOSIO_API_KEY")
user_id = os.getenv("USER_ID")

if not api_key:
    raise RuntimeError("COMPOSIO_API_KEY is not set. Create a .env file with COMPOSIO_API_KEY=your_key")

# Initialize Composio
composio = Composio(api_key=api_key, provider=OpenAIAgentsProvider())

# Create Tool Router session
session = composio.create(
    user_id=user_id,
    toolkits=["zenserp"]
)
mcp_url = session.mcp.url

# Configure agent with MCP tool
agent = Agent(
    name="Assistant",
    model="gpt-5",
    instructions=(
        "You are a helpful assistant that can access Zenserp. "
        "Help users perform Zenserp operations through natural language."
    ),
    tools=[
        HostedMCPTool(
            tool_config={
                "type": "mcp",
                "server_label": "tool_router",
                "server_url": mcp_url,
                "headers": {"x-api-key": api_key},
                "require_approval": "never",
            }
        )
    ],
)

print("\nComposio Tool Router session created.")

chat_session = SQLiteSession("conversation_openai_toolrouter")

print("\nChat started. Type your requests below.")
print("Commands: 'exit', 'quit', or 'q' to end\n")

async def main():
    try:
        result = await Runner.run(
            agent,
            "What can you help me with?",
            session=chat_session
        )
        print(f"Assistant: {result.final_output}\n")
    except Exception as e:
        print(f"Error: {e}\n")

    while True:
        user_input = input("You: ").strip()
        if user_input.lower() in {"exit", "quit", "q"}:
            print("Goodbye!")
            break

        result = await Runner.run(
            agent,
            user_input,
            session=chat_session
        )
        print(f"Assistant: {result.final_output}\n")

asyncio.run(main())

Conclusion

This was a starter code for integrating Zenserp MCP with OpenAI Agents SDK to build a functional AI agent that can interact with Zenserp.

Key features:

  • Hosted MCP tool integration through Composio's Tool Router
  • SQLite session persistence for conversation history
  • Simple async chat loop for interactive testing
You can extend this by adding more toolkits, implementing custom business logic, or building a web interface around the agent.

How to build Zenserp MCP Agent with another framework

Claude Agent SDK

Claude Agent SDK

Use Zenserp MCP with Claude Agent SDK

Google ADK

Google ADK

Use Zenserp MCP with Google ADK

LangChain

LangChain

Use Zenserp MCP with LangChain

Vercel AI SDK

Vercel AI SDK

Use Zenserp MCP with Vercel AI SDK

Mastra AI

Mastra AI

Use Zenserp MCP with Mastra AI

LlamaIndex

LlamaIndex

Use Zenserp MCP with LlamaIndex

CrewAI

CrewAI

Use Zenserp MCP with CrewAI

FAQ

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

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

Can I use Tool Router MCP with OpenAI Agents SDK?

Yes, you can. OpenAI Agents SDK 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 Zenserp tools.

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

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

Used by agents from

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ASU
Letta
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HubSpot
Agent.ai
Altera
DataStax
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Context
ASU
Letta
glean
HubSpot
Agent.ai
Altera
DataStax
Entelligence
Rolai

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