How to Use the Azure Cognitive Search MCP in AutoGen

Q: How do I add Azure Cognitive Search to AutoGen?

Install the extension using pip install -U "autogen-extmcp". Pass your Vinkius URL to mcpservertools using StreamableHttpServerParams. The adapter converts the tools for your AssistantAgent constructor.

Q: How does AutoGen handle Azure Cognitive Search schemas?

The McpToolAdapter translates the server's JSON schema into AutoGen's native format. Your agents see standard Python function definitions. They know exactly which parameters to pass for vector or lexical searches.

Give your AutoGen agents access to Azure Cognitive Search for consensus-driven retrieval.

See Vinkius in Action

Works with every AI agent you already use

…and any MCP-compatible client

MCP Servers - Free for Subscribers

Connect Azure Cognitive Search MCP to AutoGen

Create your Vinkius account to connect Azure Cognitive 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.

GDPR Free for Subscribers

Setup Azure Cognitive Search with AutoGen

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Debate MCP Server retrieval strategies

The `search_documents` tool feeds raw lexical hits into your AutoGen conversation. A researcher agent pulls full-text queries from Azure cognitive indexes and presents the findings. A secondary analyst agent immediately critiques those results and suggests alternative search terms. Multiple agents challenge each other's conclusions until they agree on the facts. If the initial query returns irrelevant documents, the group negotiates a new approach. Systems built this way rely on deliberation rather than accepting the first MCP Server response as absolute truth.

Analyze cognitive skillsets collaboratively

The `list_skillsets` tool exposes your text enrichment pipelines to the agent group. A technical agent reads the orchestration details to understand how Azure processed the text. A compliance agent reviews that same skillset to ensure no sensitive data was mistakenly translated. You build systems where competing perspectives improve data quality. Agents discuss whether the current chunking strategy makes sense for the user's question. They reach a strict consensus before formatting the final output.

Map search indexes through negotiation

The `list_indexes` tool lets your AutoGen setup discover available Azure Search resources. An explorer agent retrieves the list and proposes which index to query. A performance agent reviews the choice and might push back if the index is too large or slow. Once they agree on a target, an agent uses `get_index` to fetch the schema details. Schema conversion happens automatically via `McpToolAdapter`. Your agents understand the exact field structure without any manual mapping on your part.

Setup guide

Set up Azure Cognitive 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 Cognitive 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 Cognitive Search_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

result = await agent.run("List recent Azure Cognitive 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 Cognitive Search_assistant",
        model_client=OpenAIChatCompletionClient(model="gpt-4o"),
        workbench=workbench,
    )

    result = await agent.run("List recent Azure Cognitive 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 Cognitive 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.

Why Choose Vinkius

Vinkius connects your tools to AI with real-time monitoring and automatic cost savings — all from one dashboard.

Connect Azure Cognitive Search now

Real-time monitoring

Live

visibility into every interaction

Connect your favorite tools to your AI and see exactly what's happening — every request, every response, in real time.

Built-in savings

60%

lower AI costs

Vinkius compresses data between your apps and your AI automatically. Lower bills every month — no configuration required.

Single dashboard

One

place for every integration

Every tool your AI connects to, managed from a single screen. One account, complete control.

Common questions about Azure Cognitive Search MCP in AutoGen

Install the extension using `pip install -U "autogen-ext[mcp]"`. Pass your Vinkius URL to `mcp_server_tools` using `StreamableHttpServerParams`. The adapter converts the tools for your `AssistantAgent` constructor.

Yes. You assign the tool list to specific agents within your group chat. One agent can execute `search_documents` while another evaluates the output. They share the retrieved data through the conversation history.

The `McpToolAdapter` translates the server's JSON schema into AutoGen's native format. Your agents see standard Python function definitions. They know exactly which parameters to pass for vector or lexical searches.

The extension supports both standard I/O and Streamable HTTP transports. You choose the protocol when initializing the server parameters. Most cloud deployments rely on the HTTP transport for remote connections.

The server accesses your cognitive skillsets, text enrichments, and full-text query results. Vinkius manages the authentication but never logs the payload. The connection runs in an ephemeral sandbox and terminates completely when your AutoGen chat ends.

Use it with your favorite AI tools

Connect this server to Cursor, Claude, VS Code, and more.

OpenAI Agents SDK sdk-python

Google ADK sdk-python

Pydantic AI sdk-python

Vercel AI SDK sdk-typescript