How to Use the Azure AI Search MCP in AutoGen

Debate and refine search queries using Azure AI Search tools in AutoGen multi-agent systems.

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Connect Azure AI Search MCP to AutoGen

Create your Vinkius account to connect Azure AI Search to AutoGen 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 AutoGen

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Perplexity

Debate Azure AI Search results in AutoGen

Let your agents argue over the results of `search_documents`. One agent might challenge the relevance of the findings while another suggests a new query. This consensus-driven approach ensures the final answer is verified by multiple perspectives. It prevents the system from settling on a low-quality search match.

Coordinate Azure AI Search tasks across AutoGen agents

Assign the `vector_search` tool to a specialized agent. This agent negotiates with the rest of the team to decide when to perform a high-precision lookup. Your agents coordinate their actions to minimize unnecessary calls. They work together to pull only the relevant data needed for their common goal.

Check Azure AI Search status with AutoGen

Use `list_datasources` to confirm the availability of your data sources before starting a complex task. An agent performs this check to avoid runtime errors. If a source is missing, the agent reports the issue to the human user. It keeps your multi-agent system informed about the underlying infrastructure state.

Setup guide

Set up Azure AI Search MCP in AutoGen

Prerequisites

Python 3.10+ installed
autogen-ext[mcp] package
Active Vinkius subscription with a valid endpoint token

1

Install AutoGen with MCP
Run pip install "autogen-ext[mcp]" autogen-agentchat. The MCP extension includes mcp_server_tools for stateless tool access.
2

Fetch tools from the MCP
Call mcp_server_tools(SseServerParams(url=...)) with your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Run your agent
Pass the tools to AssistantAgent and call agent.run(). The agent invokes Azure AI Search tools and returns structured results.

agent.py

from autogen_ext.tools.mcp import SseServerParams, mcp_server_tools
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient

server_params = SseServerParams(
    url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)

tools = await mcp_server_tools(server_params)

agent = AssistantAgent(
    name="Azure AI Search_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

result = await agent.run("List recent Azure AI Search data")
print(result.messages[-1].content)

Get your connection token →

Prerequisites

Python 3.10+ installed
autogen-ext[mcp] + autogen-agentchat
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Same packages as above. McpWorkbench is ideal when your agent needs stateful sessions across multiple tool calls.
2

Use McpWorkbench as context manager
Wrap your agent in async with McpWorkbench(...) to maintain shared state and resources. The workbench manages the full MCP session lifecycle.
3

Run with workbench
Pass workbench=workbench to your agent. State is preserved across multiple tool calls within the same session.

agent.py

from autogen_ext.tools.mcp import McpWorkbench, SseServerParams
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient

server_params = SseServerParams(
    url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)

async with McpWorkbench(server_params) as workbench:
    agent = AssistantAgent(
        name="Azure AI Search_assistant",
        model_client=OpenAIChatCompletionClient(model="gpt-4o"),
        workbench=workbench,
    )

    result = await agent.run("List recent Azure AI Search data")
    print(result.messages[-1].content)

Get your connection token →

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 AutoGen

Use the MCP tool adapter to register the server functions. These tools become available for any agent in your AutoGen group chat.

Yes, the conversation framework allows agents to challenge the output of any tool. They debate the relevance of the data returned from your Azure index.

The adapter handles the schema conversion and transport. Your agents simply call the tools as if they were local functions.

Data is only shared within the conversation context of your agents. Vinkius ensures the connection remains isolated to your specific session.

Access is scoped to the tools provided to the agent group. The server restricts requests to your indexed documents, ensuring that only authorized data enters your agent debate.

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