How to Use the Azure Synapse Analytics MCP in OpenAI Agents SDK

Q: Can my OpenAI Agents SDK agent run Synapse notebooks directly?

No, this server only exposes the listnotebooks tool to inspect existing Spark notebooks. Your agent can identify which notebooks are available in the workspace, but it cannot execute them or modify their code.

Q: Does the Azure Synapse Analytics MCP Server support schema discovery in the OpenAI Agents SDK?

Yes, the agent queries listdatasets to discover metadata dynamically at runtime. This allows your agent to adapt to schema changes in your Synapse workspace without you writing custom parsing code.

Control Azure Synapse integration pipelines and compute pools directly from your OpenAI Agents SDK production workflows.

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Connect Azure Synapse Analytics MCP to OpenAI Agents SDK

Create your Vinkius account to connect Azure Synapse Analytics to OpenAI Agents SDK 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 OpenAI Agents SDK

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Map Synapse Pipelines with OpenAI Agents SDK

The `list_pipelines` tool gives your agent immediate visibility into every data integration workflow running in your Synapse workspace. Your agent calls this MCP tool to audit active pipelines, then uses `get_pipeline` to pull the JSON structure of a specific run when debugging production failures. Because the OpenAI Agents SDK handles agent-to-agent handoffs, a supervisor agent detects a failed run and passes the pipeline JSON directly to a debugging agent. The debugging agent analyzes the activity steps without manual copy-pasting.

Audit Workspace Compute Resources

The `list_spark_pools` tool inspects your active Apache Spark compute configurations to verify scale settings. Your agent pairs this with `list_sql_pools` to check if database resources are online before triggering heavy analytical queries. Deploying production OpenAI Agents SDK pipelines lets you write guardrails that intercept these tool calls. This prevents the agent from running queries if the dedicated SQL pool is paused or under-provisioned.

Inspect Data Sources and Connections

The `list_linked_services` tool exposes the connection definitions linking your Synapse workspace to external data stores. Your agent uses this alongside `list_datasets` to map out where your analytical data originates before running any transformations. The OpenAI dashboard tracks these MCP tool execution flows, letting you audit exactly when and why your agent inspected a specific linked service. You get a clean audit trail of your data architecture queries.

Setup guide

Set up Azure Synapse Analytics MCP in OpenAI Agents SDK

Prerequisites

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

1

Install the SDK

Run pip install openai-agents to install the OpenAI Agents SDK. The MCP integration is built-in — no extra dependencies needed.
2

Connect via SSE transport

Use MCPServerSse with your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. The SDK auto-discovers all Azure Synapse Analytics tools at runtime.
3

Create your Agent

Pass the MCP to Agent(mcp_servers=[server]). The agent receives Azure Synapse Analytics tools as native definitions — JSON schemas resolve automatically.
4

Run the agent

Call Runner.run(agent, prompt) to execute. The agent invokes the appropriate Azure Synapse Analytics tools and returns structured results. Copy the full example on the right to get started.

agent.py

import asyncio
from agents import Agent, Runner
from agents.mcp import MCPServerSse

async def main():
    async with MCPServerSse(
        url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    ) as server:
        agent = Agent(
            name="Azure Synapse Analytics Agent",
            instructions="You have access to Azure Synapse Analytics tools.",
            mcp_servers=[server],
        )
        result = await Runner.run(agent, "List recent transactions")
        print(result.final_output)

asyncio.run(main())

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Common questions about Azure Synapse Analytics MCP in OpenAI Agents SDK

Install openai-agents via pip and configure the streamable HTTP server using MCPServerStreamableHttp. Pass this server instance in the mcp_servers list during your Agent initialization to let your agent auto-discover the Synapse tools.

No, this server only exposes the `list_notebooks` tool to inspect existing Spark notebooks. Your agent can identify which notebooks are available in the workspace, but it cannot execute them or modify their code.

You define validation functions that run before the agent executes tools like `list_sql_pools`. If the agent attempts to inspect an unauthorized pool, your guardrail blocks the tool call before it hits the Azure API.

Yes, the agent queries `list_datasets` to discover metadata dynamically at runtime. This allows your agent to adapt to schema changes in your Synapse workspace without you writing custom parsing code.

The server only touches Synapse metadata, such as pipeline definitions and linked service names, never your raw analytical data. Vinkius runs the server in a sandboxed V8 isolate, ensuring your Azure credentials and workspace connection strings remain ephemeral and never persist on disk.

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