How to Use the Azure Synapse Analytics MCP in CrewAI

Q: How do I link Azure Synapse Analytics to CrewAI?

Run pip install crewai "crewaitools". For basic setup, pass your server URL directly into the mcps list when defining your agent.

Q: Can I restrict which Azure Synapse Analytics tools a CrewAI agent uses?

You can limit access by using the MCPServerHTTP class. Pass a toolfilter to ensure specific workers only see pipeline tools while others handle compute pools.

Deploy autonomous agent teams to monitor and audit your Azure Synapse Analytics environments with CrewAI.

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Connect Azure Synapse Analytics MCP to CrewAI

Create your Vinkius account to connect Azure Synapse Analytics 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 Synapse Analytics with CrewAI

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Map Synapse pipelines with CrewAI agents

You assign the `get_pipeline` and `list_pipelines` tools to a specialized research agent. This worker continuously queries your data integration structures and shares the exact pipeline definitions with the rest of the crew through shared memory. Setup requires passing the Vinkius endpoint directly into the `mcps` array of your agent configuration. A moderator agent then evaluates these pipeline states to determine if data movement tasks require escalation.

Track compute pools autonomously

Giving a monitor agent access to `list_spark_pools` and `list_sql_pools` creates an independent infrastructure watch system. The worker checks dedicated and serverless SQL availability while another agent analyzes the Apache Spark pool provisioning. If you need strict control over what each worker sees, you import `MCPServerHTTP` from `crewai.mcp`. Applying a `tool_filter` ensures your monitoring agent only executes compute checks and ignores other endpoint capabilities.

Audit datasets as a team

The `list_datasets` and `list_linked_services` operations allow an audit agent to verify explicit storage targets and external connections. It pairs this data with `list_notebooks` to build a complete map of your Spark notebook dependencies. These specialized workers operate sequentially or hierarchically depending on your chosen execution mode. They handle the entire verification process without requiring human intervention.

Setup guide

Set up Azure Synapse Analytics 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 Synapse Analytics tools as needed.

crew.py

from crewai import Agent, Task, Crew

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

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

    task = Task(
        description="List recent Azure Synapse Analytics 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 Synapse Analytics. 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 Synapse Analytics MCP in CrewAI

Run `pip install crewai "crewai[tools]"`. For basic setup, pass your server URL directly into the `mcps` list when defining your agent.

You can limit access by using the `MCPServerHTTP` class. Pass a `tool_filter` to ensure specific workers only see pipeline tools while others handle compute pools.

The tools feed results directly into the framework's memory system. When one agent lists your Spark notebooks, the other agents can reference that exact list in subsequent tasks.

The framework supports stdio, SSE, and Streamable HTTP transports. You provide the HTTP endpoint from Vinkius and the system establishes the connection.

We route your requests through a zero-trust V8 Isolate Sandbox. The server queries your data integration jobs and explicit datasets, passes the JSON back to your crew, and immediately burns the environment down.

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