How to Use the Vertex AI Vector Search MCP in OpenAI Agents SDK

Q: How do I use Vertex AI Vector Search with OpenAI Agents SDK?

The agent executes searchnearestneighbors by taking the necessary endpoint ID, index ID, and your query vector. It returns the nearest neighbors found in the semantic embeddings.

Q: Can I use Vertex AI Vector Search with OpenAI Agents SDK to list all indexes?

Yep. The listvectorindexes tool lets your agent pull a complete manifest of every vector index deployed in the project, giving you visibility into what's available for searching.

Q: Does Vertex AI Vector Search help with long-running operations when using OpenAI Agents SDK?

Yes. The listvectoroperations tool lets your agent track long-running jobs, ensuring that complex index builds or updates complete successfully before you try to search them.

Give your OpenAI Agents SDK access to semantic search across billions of vectors.

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Connect Vertex AI Vector Search MCP to OpenAI Agents SDK

Create your Vinkius account to connect Vertex AI Vector Search to OpenAI Agents SDK and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

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Setup Vertex AI Vector Search with OpenAI Agents SDK

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Search Nearest Neighbors with MCP Server

The `search_nearest_neighbors` tool lets your agent perform vector similarity searches. You pass an endpoint ID, a deployed index ID, and the query vector as JSON. This means your agent doesn't just talk about data—it actually queries semantic embeddings stored in Vertex AI.

Manage Indexes via OpenAI Agents SDK

Need to check what indexes are available? Use `list_vector_indexes` to get a full list of all vector indexes within your Google Cloud project. You can also use `get_index_details` to pull specific metadata and configuration for any index you're targeting.

Check Index Endpoints Status

The `list_index_endpoints` tool lets your agent confirm which endpoints are active in the project. This is crucial before running a search, ensuring the connection points you're using for vector matching are currently deployed and ready to go.

Setup guide

Set up Vertex AI Vector Search MCP in OpenAI Agents SDK

Prerequisites

Python 3.10+ installed
openai-agents package (pip install openai-agents)
Active Vinkius subscription with a valid endpoint token

1

Install the SDK

Run pip install openai-agents to install the OpenAI Agents SDK. The MCP integration is built-in — no extra dependencies needed.
2

Connect via SSE transport

Use MCPServerSse with your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. The SDK auto-discovers all Vertex AI Vector Search tools at runtime.
3

Create your Agent

Pass the MCP to Agent(mcp_servers=[server]). The agent receives Vertex AI Vector Search tools as native definitions — JSON schemas resolve automatically.
4

Run the agent

Call Runner.run(agent, prompt) to execute. The agent invokes the appropriate Vertex AI Vector Search tools and returns structured results. Copy the full example on the right to get started.

agent.py

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

async def main():
    async with MCPServerSse(
        url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    ) as server:
        agent = Agent(
            name="Vertex AI Vector Search Agent",
            instructions="You have access to Vertex AI Vector Search tools.",
            mcp_servers=[server],
        )
        result = await Runner.run(agent, "List recent transactions")
        print(result.final_output)

asyncio.run(main())

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Vertex AI Vector Search. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

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Common questions about Vertex AI Vector Search MCP in OpenAI Agents SDK

The agent executes `search_nearest_neighbors` by taking the necessary endpoint ID, index ID, and your query vector. It returns the nearest neighbors found in the semantic embeddings.

This MCP Server handles sensitive structured data like index metadata, operational configuration details, and raw query vectors.

Yep. The `list_vector_indexes` tool lets your agent pull a complete manifest of every vector index deployed in the project, giving you visibility into what's available for searching.

Yes. The `list_vector_operations` tool lets your agent track long-running jobs, ensuring that complex index builds or updates complete successfully before you try to search them.

Absolutely. Since the OpenAPI Agents SDK includes built-in guardrails and full tracing, running this MCP Server as part of your agent system keeps actions validated and observable.

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