How to Use the Azure AI Search MCP in CrewAI

Q: Does this MCP server allow CrewAI to perform vector searches on Azure AI Search?

Yes. Add vectorsearch to your agent tools. Your crew can then perform high-precision similarity searches against your Azure indexes.

Deploy specialized agent crews with CrewAI to monitor and search your Azure AI Search indexes automatically.

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Connect Azure AI Search MCP to CrewAI

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

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Perplexity

Specialized indexing crews for CrewAI

Assign one agent to `list_indexes` while another performs the actual search. CrewAI manages the collaboration between these roles efficiently. This keeps your operations organized. Each agent has a single, clear responsibility.

Vector relevance analysis in CrewAI

Let your research agent call `vector_search` to find relevant documentation. The findings are then shared with your analysis agent within the crew's memory. It makes deep-dive research fast. You don't need human oversight to get the initial results.

Full-text retrieval for CrewAI agents

Use `search_documents` to gather raw data for your agents to process. CrewAI handles the sequential execution of these searches across your crew. Your agents act on the data immediately. It mimics a human analyst's workflow.

Setup guide

Set up Azure AI Search MCP in CrewAI

Prerequisites

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

1

Install CrewAI
Run pip install crewai to install the framework. MCP support is built-in via the mcps parameter.
2

Add the MCP URL to your agent
Pass your Vinkius endpoint directly to the mcps list. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. CrewAI handles tool discovery and caching automatically.
3

Kick off your crew
Create a Crew with your agent and tasks. Call crew.kickoff() — the agent will automatically invoke Azure AI Search tools as needed.

crew.py

from crewai import Agent, Task, Crew

agent = Agent(
    role="Azure AI Search Analyst",
    goal="Access and analyze Azure AI Search data via MCP.",
    backstory="Expert analyst with direct Azure AI Search access.",
    mcps=[
        "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    ],
)

task = Task(
    description="List recent Azure AI Search transactions",
    agent=agent,
    expected_output="A summary of recent activity",
)

crew = Crew(agents=[agent], tasks=[task])
result = crew.kickoff()
print(result)

Get your connection token →

Prerequisites

Python 3.10+ installed
crewai + crewai-tools packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install crewai crewai-tools. The MCPServerAdapter handles lifecycle management and tool conversion.
2

Connect with MCPServerAdapter
Use MCPServerAdapter as a context manager with SseServerParameters pointing to your Vinkius endpoint. The adapter automatically manages connection lifecycle.
3

Assign tools and run
Pass the returned mcp_tools to your agent's tools parameter. The adapter converts MCP tools to native BaseTool objects compatible with all CrewAI agents.

crew.py

from crewai import Agent, Task, Crew
from crewai_tools import MCPServerAdapter
from mcp import SseServerParameters

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

with MCPServerAdapter(server_params) as mcp_tools:
    agent = Agent(
        role="Azure AI Search Analyst",
        goal="Access and analyze Azure AI Search data via MCP.",
        backstory="Expert analyst with direct Azure AI Search access.",
        tools=mcp_tools,
    )

    task = Task(
        description="List recent Azure AI Search transactions",
        agent=agent,
        expected_output="A summary of recent activity",
    )

    crew = Crew(agents=[agent], tasks=[task])
    result = crew.kickoff()
    print(result)

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 CrewAI

Define the tools in your agent configuration by pointing to the server URL. Your agents can then call `search_documents` as part of their assigned tasks.

Yes. Because CrewAI uses shared memory, once one agent retrieves data, the rest of the crew can act on that information.

You can use tool filtering to restrict which agents have access to specific indexes. This keeps your agent roles focused and secure.

Yes. Add `vector_search` to your agent tools. Your crew can then perform high-precision similarity searches against your Azure indexes.

Vinkius uses an ephemeral sandbox for the connection. Access is restricted to the specific tools you enable for your agents.

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