Does Marqo handle the vector embeddings for me through the agent?

Yes. Marqo is an end-to-end engine. When you use the `tensor_search` tool, you provide natural language and Marqo handles the model inference and vector extraction under the hood, returning semantically relevant results immediately.

Can I add new data to a vector index through a conversation?

Absolutely. Use the `add_documents` tool by providing a JSON array of your documents. Your agent will synchronize these records into the target index, and they will be searchable via semantic query instantly.

How do I check the stats of my vector index?

The `get_index_stats` tool retrieves critical metrics for a specific index. Your agent will report the document count, memory usage, and configuration details, helping you monitor the health of your vector store.

Marqo AI (Vector Search & Embeddings) MCP Server for OpenAI Agents SDK 6 tools — connect in under 2 minutes

Name: Marqo AI (Vector Search & Embeddings)
Availability: InStock
Author: Vinkius

openai.github.io/openai-agents-python/mcp

Built by Vinkius GDPR 6 Tools SDK

The OpenAI Agents SDK enables production-grade agent workflows in Python. Connect Marqo AI (Vector Search & Embeddings) through the Vinkius and your agents gain typed, auto-discovered tools with built-in guardrails — no manual schema definitions required.

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

Open in ChatGPT Open in Claude Open in Perplexity

Vinkius supports streamable HTTP and SSE.

python

import asyncio
from agents import Agent, Runner
from agents.mcp import MCPServerStreamableHttp

async def main():
    # Your Vinkius token — get it at cloud.vinkius.com
    async with MCPServerStreamableHttp(
        url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    ) as mcp_server:

        agent = Agent(
            name="Marqo AI (Vector Search & Embeddings) Assistant",
            instructions=(
                "You help users interact with Marqo AI (Vector Search & Embeddings). "
                "You have access to 6 tools."
            ),
            mcp_servers=[mcp_server],
        )

        result = await Runner.run(
            agent, "List all available tools from Marqo AI (Vector Search & Embeddings)"
        )
        print(result.final_output)

asyncio.run(main())

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About Marqo AI (Vector Search & Embeddings) MCP Server

Connect your Marqo instance to any AI agent and take full control of your semantic search infrastructure, vector embeddings, and real-time document indexing through natural conversation.

The OpenAI Agents SDK auto-discovers all 6 tools from Marqo AI (Vector Search & Embeddings) through native MCP integration. Build agents with built-in guardrails, tracing, and handoff patterns — chain multiple agents where one queries Marqo AI (Vector Search & Embeddings), another analyzes results, and a third generates reports, all orchestrated through the Vinkius.

What you can do

Tensor Search Orchestration — Execute dense semantic similarity searches against your indices using natural language queries, with Marqo handling embedding extraction automatically
Dynamic Document Ingestion — Write new JSON records into your vector indices directly from your agent, allowing for instant searchability of fresh data mappings
Index Lifecycle Management — Create explicitly bounded new vector indices with custom model settings and dimension constraints to optimize your search architecture
Vector Audit & Stats — Retrieve detailed configuration metrics for your indices, including document counts, embedding model types, and underlying schema mappings
Precision Deletion — Physically eradicate vectorized representations by targeting specific scalar identifiers to maintain a clean and relevant search index
Resource Inventory — List all available vector indices on your Marqo instance to identify collection boundaries before executing search queries

The Marqo AI (Vector Search & Embeddings) MCP Server exposes 6 tools through the Vinkius. Connect it to OpenAI Agents SDK in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.

How to Connect Marqo AI (Vector Search & Embeddings) to OpenAI Agents SDK via MCP

Follow these steps to integrate the Marqo AI (Vector Search & Embeddings) MCP Server with OpenAI Agents SDK.

Install the SDK

Run pip install openai-agents in your Python environment

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token from cloud.vinkius.com

Run the script

Save the code above and run it: python agent.py

Explore tools

The agent will automatically discover 6 tools from Marqo AI (Vector Search & Embeddings)

Why Use OpenAI Agents SDK with the Marqo AI (Vector Search & Embeddings) MCP Server

OpenAI Agents SDK provides unique advantages when paired with Marqo AI (Vector Search & Embeddings) through the Model Context Protocol.

Native MCP integration via `MCPServerSse` — pass the URL and the SDK auto-discovers all tools with full type safety

Built-in guardrails, tracing, and handoff patterns let you build production-grade agents without reinventing safety infrastructure

Lightweight and composable: chain multiple agents and MCP servers in a single pipeline with minimal boilerplate

First-party OpenAI support ensures optimal compatibility with GPT models for tool calling and structured output

Marqo AI (Vector Search & Embeddings) + OpenAI Agents SDK Use Cases

Practical scenarios where OpenAI Agents SDK combined with the Marqo AI (Vector Search & Embeddings) MCP Server delivers measurable value.

Automated workflows: build agents that query Marqo AI (Vector Search & Embeddings), process the data, and trigger follow-up actions autonomously

Multi-agent orchestration: create specialist agents — one queries Marqo AI (Vector Search & Embeddings), another analyzes results, a third generates reports

Data enrichment pipelines: stream data through Marqo AI (Vector Search & Embeddings) tools and transform it with OpenAI models in a single async loop

Customer support bots: agents query Marqo AI (Vector Search & Embeddings) to resolve tickets, look up records, and update statuses without human intervention

Marqo AI (Vector Search & Embeddings) MCP Tools for OpenAI Agents SDK (6)

These 6 tools become available when you connect Marqo AI (Vector Search & Embeddings) to OpenAI Agents SDK via MCP:

add_documents

Write new documents into Marqo

create_index

Create an explicitly bounded new vector index

delete_documents

Delete specific documents from Marqo by targeting their IDs

get_index_stats

Get configuration and stats for an index

list_indexes

Crucial before writing queries hitting arbitrary collections. List all Marqo vector indexes

tensor_search

Perform natural language tensor search on Marqo

Example Prompts for Marqo AI (Vector Search & Embeddings) in OpenAI Agents SDK

Ready-to-use prompts you can give your OpenAI Agents SDK agent to start working with Marqo AI (Vector Search & Embeddings) immediately.

"Semantic search in index 'products' for 'lightweight running shoes for trails'"

"List all vector indexes in my Marqo instance"

"Add this document to the 'support-docs' index: {"title": "API Auth", "content": "Use Marqo-API-Key header"}"

Troubleshooting Marqo AI (Vector Search & Embeddings) MCP Server with OpenAI Agents SDK

Common issues when connecting Marqo AI (Vector Search & Embeddings) to OpenAI Agents SDK through the Vinkius, and how to resolve them.

MCPServerStreamableHttp not found

Ensure you have the latest version: pip install --upgrade openai-agents

Agent not calling tools

Make sure your prompt explicitly references the task the tools can help with.

Marqo AI (Vector Search & Embeddings) + OpenAI Agents SDK FAQ

Common questions about integrating Marqo AI (Vector Search & Embeddings) MCP Server with OpenAI Agents SDK.

How does the OpenAI Agents SDK connect to MCP?

Use MCPServerSse(url=...) to create a server connection. The SDK auto-discovers all tools and makes them available to your agent with full type information.