How to Use the Azure Cognitive Search MCP in CrewAI

Q: How do I assign Azure Cognitive Search tools to a specific CrewAI agent?

Use the toolfilter in MCPServerHTTP to expose only searchdocuments to your researcher agent, leaving other tools locked.

Q: Can CrewAI agents share Azure Cognitive Search results with each other?

Yes. CrewAI's shared memory lets one agent run getdocument and pass the document content to other agents in the crew.

Q: How do I configure the Azure Cognitive Search MCP Server URL in CrewAI?

Pass the Vinkius endpoint URL directly into the mcps list when initializing your CrewAI agents for a simple setup.

Q: Can a CrewAI moderator agent stop an indexer?

No. The server only exposes read and query tools like listindexers and searchdocuments, so agents cannot modify your Azure infrastructure.

Deploy specialized CrewAI agent teams to monitor, search, and analyze your Azure indexes autonomously.

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Connect Azure Cognitive Search MCP to CrewAI

Create your Vinkius account to connect Azure Cognitive 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 Cognitive Search with CrewAI

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Perplexity

Divide search and analysis tasks across CrewAI agents

`search_documents` runs lexical full-text queries for your research agent while a separate analyst agent uses the results to write reports. This division of labor keeps your CrewAI execution fast and organized. Because this MCP Server integrates with CrewAI's shared memory, the analyst agent remembers previous search results. This prevents duplicate queries and keeps API costs low.

CrewAI monitors and tunes cognitive skillsets

`list_skillsets` allows a monitor agent to audit text enrichment pipelines while a moderator agent logs performance issues. The crew works together to flag broken AI enrichments in your Azure index. By exposing these tools selectively with a `tool_filter`, you ensure your agents only touch the specific indexers and skillsets they need to do their jobs.

Run hybrid search strategies in CrewAI teams

`vector_search` provides KNN structural matches to your technical agent, which then compares those hits against lexical results from `search_documents`. This MCP Server allows your technical agent to build a complete data profile instantly. This multi-agent approach ensures your autonomous workflows get the most relevant document matches before executing downstream actions like automated customer support.

Setup guide

Set up Azure Cognitive 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 Cognitive Search tools as needed.

crew.py

from crewai import Agent, Task, Crew

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

task = Task(
    description="List recent Azure Cognitive 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 Cognitive Search Analyst",
        goal="Access and analyze Azure Cognitive Search data via MCP.",
        backstory="Expert analyst with direct Azure Cognitive Search access.",
        tools=mcp_tools,
    )

    task = Task(
        description="List recent Azure Cognitive 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 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.

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Common questions about Azure Cognitive Search MCP in CrewAI

Use the `tool_filter` in `MCPServerHTTP` to expose only `search_documents` to your researcher agent, leaving other tools locked.

Yes. CrewAI's shared memory lets one agent run `get_document` and pass the document content to other agents in the crew.

Pass the Vinkius endpoint URL directly into the `mcps` list when initializing your CrewAI agents for a simple setup.

No. The server only exposes read and query tools like `list_indexers` and `search_documents`, so agents cannot modify your Azure infrastructure.

All document contents and index configurations remain private. The connection runs over HTTPS to an ephemeral V8 sandbox, preventing credentials or data from leaking between agent runs.

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