Vertex AI Vector Search MCP Server for OpenAI Agents SDK 6 tools — connect in under 2 minutes
The OpenAI Agents SDK enables production-grade agent workflows in Python. Connect Vertex AI Vector Search through the Vinkius and your agents gain typed, auto-discovered tools with built-in guardrails — no manual schema definitions required.
ASK AI ABOUT THIS MCP SERVER
Vinkius supports streamable HTTP and SSE.
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="Vertex AI Vector Search Assistant",
instructions=(
"You help users interact with Vertex AI Vector Search. "
"You have access to 6 tools."
),
mcp_servers=[mcp_server],
)
result = await Runner.run(
agent, "List all available tools from Vertex AI Vector Search"
)
print(result.final_output)
asyncio.run(main())
* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure
About Vertex AI Vector Search MCP Server
Plug the sheer matching scale of Google Cloud's Vertex AI Vector Search directly into your intelligent IDE or conversational agent. Unleash low-latency nearest neighbor lookups across billion-scale embedding structures without navigating Cloud Console interfaces.
The OpenAI Agents SDK auto-discovers all 6 tools from Vertex AI Vector Search through native MCP integration. Build agents with built-in guardrails, tracing, and handoff patterns — chain multiple agents where one queries Vertex AI Vector Search, another analyzes results, and a third generates reports, all orchestrated through the Vinkius.
What you can do
- Massive Semantic Extraction — Prompt your agent to formulate query vectors and blast them at your specialized Cloud endpoints. It instantly retrieves identical geometric text boundaries (nearest neighbors) to ground LLM contexts powerfully.
- Index Operations — Gain total situational awareness over your massive datasets. Command the bot to list your provisioned Vector Indexes, verifying dimensionality, configuration updates, and current active states within seconds.
- Endpoint Monitoring — List active network endpoints scaling your specific RAG applications. Determine clearly which underlying deployed index iterations are currently receiving production traffic without digging through IAM screens.
- Operation Tracking — Spun up a multi-terabyte index build? Query the cloud queue using chat to review persistent long-running task timelines from your primary editor.
The Vertex AI Vector Search 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 Vertex AI Vector Search to OpenAI Agents SDK via MCP
Follow these steps to integrate the Vertex AI Vector Search 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 Vertex AI Vector Search
Why Use OpenAI Agents SDK with the Vertex AI Vector Search MCP Server
OpenAI Agents SDK provides unique advantages when paired with Vertex AI Vector Search 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
Vertex AI Vector Search + OpenAI Agents SDK Use Cases
Practical scenarios where OpenAI Agents SDK combined with the Vertex AI Vector Search MCP Server delivers measurable value.
Automated workflows: build agents that query Vertex AI Vector Search, process the data, and trigger follow-up actions autonomously
Multi-agent orchestration: create specialist agents — one queries Vertex AI Vector Search, another analyzes results, a third generates reports
Data enrichment pipelines: stream data through Vertex AI Vector Search tools and transform it with OpenAI models in a single async loop
Customer support bots: agents query Vertex AI Vector Search to resolve tickets, look up records, and update statuses without human intervention
Vertex AI Vector Search MCP Tools for OpenAI Agents SDK (6)
These 6 tools become available when you connect Vertex AI Vector Search to OpenAI Agents SDK via MCP:
get_index_details
Retrieves metadata and configuration for a specific vector index
list_deployed_indexes
Lists all indexes deployed to a specific endpoint
list_index_endpoints
Lists all index endpoints in the project
list_vector_indexes
Lists all vector indexes in the Google Cloud project
list_vector_operations
Lists long-running operations related to vector indexes
search_nearest_neighbors
Provide the endpoint ID, deployed index ID, and a query vector as a JSON array. Performs a nearest neighbor vector similarity search
Example Prompts for Vertex AI Vector Search in OpenAI Agents SDK
Ready-to-use prompts you can give your OpenAI Agents SDK agent to start working with Vertex AI Vector Search immediately.
"List all our active vector indexes on the current GCP project."
"Check for any long-running vector deployment operations currently uncompleted."
"Find the 3 nearest neighbors mapping to endpoint '39xl' array index ID 'dep_30' using vector [-0.2, 0.5, 0.0]."
Troubleshooting Vertex AI Vector Search MCP Server with OpenAI Agents SDK
Common issues when connecting Vertex AI Vector Search to OpenAI Agents SDK through the Vinkius, and how to resolve them.
MCPServerStreamableHttp not found
pip install --upgrade openai-agentsAgent not calling tools
Vertex AI Vector Search + OpenAI Agents SDK FAQ
Common questions about integrating Vertex AI Vector Search MCP Server with OpenAI Agents SDK.
How does the OpenAI Agents SDK connect to MCP?
MCPServerSse(url=...) to create a server connection. The SDK auto-discovers all tools and makes them available to your agent with full type information.Can I use multiple MCP servers in one agent?
MCPServerSse instances to the agent constructor. The agent can use tools from all connected servers within a single run.Does the SDK support streaming responses?
Connect Vertex AI Vector Search with your favorite client
Step-by-step setup guides for every MCP-compatible client and framework:
Anthropic's native desktop app for Claude with built-in MCP support.
AI-first code editor with integrated LLM-powered coding assistance.
GitHub Copilot in VS Code with Agent mode and MCP support.
Purpose-built IDE for agentic AI coding workflows.
Autonomous AI coding agent that runs inside VS Code.
Anthropic's agentic CLI for terminal-first development.
Python SDK for building production-grade OpenAI agent workflows.
Google's framework for building production AI agents.
Type-safe agent development for Python with first-class MCP support.
TypeScript toolkit for building AI-powered web applications.
TypeScript-native agent framework for modern web stacks.
Python framework for orchestrating collaborative AI agent crews.
Leading Python framework for composable LLM applications.
Data-aware AI agent framework for structured and unstructured sources.
Microsoft's framework for multi-agent collaborative conversations.
Connect Vertex AI Vector Search to OpenAI Agents SDK
Get your token, paste the configuration, and start using 6 tools in under 2 minutes. No API key management needed.