How to Use the Azure AI Search MCP in OpenAI Agents SDK

Q: Can the OpenAI Agents SDK run hybrid queries on Azure AI Search?

Yes. The agent combines vectorsearch for semantic intent and searchdocuments for exact keyword matches. It decides which tool to call based on the user's prompt.

Connect Azure AI Search to your OpenAI Agents SDK system. Run vector searches and full-text queries safely with built-in guardrails.

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Connect Azure AI Search MCP to OpenAI Agents SDK

Create your Vinkius account to connect Azure AI 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 Azure AI Search with OpenAI Agents SDK

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Query Azure AI Search with OpenAI Agents SDK

Your production agent needs real data to avoid hallucinations. Hook up this MCP Server and it can immediately run `vector_search` against your Azure embedding spaces. The agent decides when to trigger a search based on user prompts. If someone asks for specific documents, it fires off `search_documents` for lexical full-text retrieval and feeds the results straight back into the conversation context.

Audit indexes before execution

Blindly querying unknown databases causes runtime errors. Your agent can map out the entire cloud environment first by calling `list_indexes` and `list_datasources`. Once it knows what exists, it pulls exact schemas using `get_index`. OpenAI's built-in tracing logs every single one of these MCP discovery steps on your dashboard so you know exactly how the agent builds its context.

Monitor scheduled indexer tasks

Stale data ruins RAG applications. Instead of guessing if your database is current, the agent runs `list_indexers` to check the status of Azure scheduled tasks. If an indexer failed overnight, your system catches it before answering a user query. You get strict safety constraints because the agent validates the data pipeline's health before it ever attempts a semantic search.

Setup guide

Set up Azure AI 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 Azure AI Search tools at runtime.
3

Create your Agent

Pass the MCP to Agent(mcp_servers=[server]). The agent receives Azure AI 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 Azure AI 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="Azure AI Search Agent",
            instructions="You have access to Azure AI 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 Azure AI 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 Azure AI Search MCP in OpenAI Agents SDK

Pass the Vinkius endpoint to MCPServerStreamableHttp in your Python code. Assign the MCP server to the mcp_servers array in your Agent constructor and set cacheToolsList=True for faster cold starts.

Yes. The agent combines `vector_search` for semantic intent and `search_documents` for exact keyword matches. It decides which tool to call based on the user's prompt.

Yes, it fits right into the handoff model. You can dedicate one specialized agent to managing search queries, and other agents simply hand off tasks to it when they need fresh cloud data.

OpenAI's guardrails catch the error before it breaks the user experience. You will see the exact API failure in your tracing dashboard and the agent can retry the tool call automatically.

We never store your full-text search strings or vector embeddings. The V8 Isolate Sandbox destroys itself the millisecond your query finishes, leaving zero trace of your intellectual property on our infrastructure.

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