How to integrate Pinecone MCP with OpenAI Agents SDK

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Introduction

This guide walks you through connecting Pinecone to the OpenAI Agents SDK using the Composio tool router. By the end, you'll have a working Pinecone agent that can query all vectors similar to user question, upsert document embeddings into a namespace, delete vectors from the archive index through natural language commands.

This guide will help you understand how to give your OpenAI Agents SDK agent real control over a Pinecone account through Composio's Pinecone 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 Pinecone
  • Configure an AI agent that can use Pinecone as a tool
  • Run a live chat session where you can ask the agent to perform Pinecone 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 Pinecone MCP server, and what's possible with it?

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

Supported Tools & Triggers

Tools
Cancel Bulk ImportTool to cancel a bulk import operation in Pinecone.
Configure IndexTool to configure an existing Pinecone index, including pod type, replicas, deletion protection, and tags.
Create BackupTool to create a backup of a Pinecone index for disaster recovery and version control.
Create IndexTool to create a Pinecone index with specified configuration.
Create Index with Embedding ModelTool to create a Pinecone index with integrated embedding model for automatic vectorization.
Create Index from BackupTool to create an index from a backup.
Create NamespaceTool to create a namespace within a serverless Pinecone index.
Delete IndexTool to permanently delete a Pinecone index.
Delete NamespaceTool to permanently delete a namespace from a serverless index.
Describe BackupTool to retrieve detailed information about a specific backup.
Describe Bulk ImportTool to describe a specific bulk import operation in Pinecone.
Describe Index StatsTool to get index statistics including vector count per namespace, dimensions, and fullness.
Describe Restore JobTool to get detailed information about a specific restore job in Pinecone.
Generate EmbeddingsTool to generate vector embeddings for input text using Pinecone's hosted embedding models.
Get Model InformationTool to retrieve detailed information about a specific model hosted by Pinecone.
List Bulk ImportsTool to list all recent and ongoing bulk import operations in Pinecone.
List CollectionsTool to list all collections in a Pinecone project (pod-based indexes only).
List Index BackupsTool to list all backups for a specific Pinecone index.
List IndexesTool to list all indexes in a Pinecone project.
List Available ModelsTool to list all available embedding and reranking models hosted by Pinecone.
List NamespacesTool to list all namespaces in a serverless Pinecone index.
List Project BackupsTool to list all backups for indexes in a Pinecone project.
List Restore JobsTool to list all restore jobs for a project with pagination support.
List VectorsTool to list vector IDs in a Pinecone serverless index.
Query VectorsTool to perform semantic search within a Pinecone index using a query vector.
Rerank DocumentsTool to rerank documents by semantic relevance to a query.
Search Records in NamespaceTool to search records within a Pinecone namespace using text, vector, or ID query.
Start Bulk ImportTool to start an asynchronous bulk import of vectors from object storage (S3, GCS, or Azure Blob Storage) into a Pinecone index.
Update VectorTool to update a vector in Pinecone by ID.
Upsert Records to NamespaceTool to upsert text records into a Pinecone namespace.

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 Pinecone 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 Pinecone.

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 Pinecone Tool Router session
session = composio.create(
    user_id=user_id,
    toolkits=["pinecone"]
)

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 pinecone.
  • The router checks the user's Pinecone connection and prepares the MCP endpoint.
  • The returned session.mcp.url is the MCP URL that your agent will use to access Pinecone.
  • This approach keeps things lightweight and lets the agent request Pinecone 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 Pinecone. "
        "Help users perform Pinecone 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 Pinecone 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 Pinecone 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 Pinecone.
  • 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 Pinecone 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=["pinecone"]
)
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 Pinecone. "
        "Help users perform Pinecone 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 Pinecone MCP with OpenAI Agents SDK to build a functional AI agent that can interact with Pinecone.

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.

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FAQ

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

With a standalone Pinecone MCP server, the agents and LLMs can only access a fixed set of Pinecone tools tied to that server. However, with the Composio Tool Router, agents can dynamically load tools from Pinecone 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 Pinecone tools.

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

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

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