How to Use the Azure Synapse Analytics MCP in AutoGen

Q: How do AutoGen agents share Azure Synapse Analytics tool outputs?

When an AutoGen agent calls a tool like listpipelines, the JSON output is posted directly into the group chat. Other agents, such as your security or optimization agents, read this context to inform their next turn in the conversation.

Q: Can I use Azure Synapse Analytics tools to automate cost optimization in AutoGen?

Yes. Your AutoGen agents can call listsqlpools and listsparkpools to audit active compute resources, debate the resource allocation, and recommend scaling actions based on live pool configurations.

Let your AutoGen agents debate and optimize your Azure Synapse Analytics pipelines and compute pools in real time.

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Connect Azure Synapse Analytics MCP to AutoGen

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

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Coordinate Multi-Agent Audits of Synapse Pipelines

The `get_pipeline` tool allows an AutoGen audit agent to inspect specific pipeline definitions over our MCP channel while a separate security agent reviews the linked services via `list_linked_services`. The agents then debate whether the pipeline conforms to your enterprise security standards. This multi-agent conversation ensures that no single agent makes a blind decision. By sharing the output of these tools in their group chat, your agents reach a consensus on whether a pipeline is safe to run.

Balance Compute Costs with AutoGen MCP Server

The `list_sql_pools` tool gives your AutoGen cost-control agent the ability to check active SQL pools, while a performance agent checks your Spark setups using `list_spark_pools`. They can argue over which compute pool is most cost-effective for an upcoming data job. Since AutoGen supports direct tool execution within agent conversations, the agents use the live compute states to settle their debate, choosing the optimal pool without requiring human intervention.

Map Data Flows via Multi-Agent Consensus

The `list_datasets` tool lets your AutoGen data-steward agent fetch all explicit dataset targets in your workspace. A separate developer agent can then call `list_pipelines` to trace how those datasets are processed. By discussing the outputs of these two tools, the agents build a shared understanding of your Synapse workspace, allowing them to flag redundant datasets or orphaned pipelines during their chat.

Setup guide

Set up Azure Synapse Analytics 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 Synapse Analytics 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 Synapse Analytics_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

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

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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 Synapse Analytics_assistant",
        model_client=OpenAIChatCompletionClient(model="gpt-4o"),
        workbench=workbench,
    )

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

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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 AutoGen

When an AutoGen agent calls a tool like `list_pipelines`, the JSON output is posted directly into the group chat. Other agents, such as your security or optimization agents, read this context to inform their next turn in the conversation.

Yes. Your AutoGen agents can call `list_sql_pools` and `list_spark_pools` to audit active compute resources, debate the resource allocation, and recommend scaling actions based on live pool configurations.

You use the autogen-ext MCP adapter to load the tools from your Vinkius endpoint, then pass the resulting tool list to your AssistantAgent constructor. The adapter handles all schema conversions automatically.

Yes, your agents can divide and conquer. One agent can inspect datasets using `list_datasets` while another audits notebooks via `list_notebooks`, combining their findings in the chat.

Your workspace credentials and Synapse resource definitions—like dataset targets and notebook paths—are handled through Vinkius's secure, one-token MCP endpoint. Your AutoGen agents never see or store your Azure secrets, and all tool executions run within an isolated sandbox.

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