How to Use the Alation MCP in AutoGen

Q: How do AutoGen agents share Alation data?

They pass the results of listtables back and forth during their conversation. This ensures both agents work from the same catalog snapshot.

Q: Does Alation provide context for AutoGen debates?

The agents call getobjectmetadata to clarify asset details during their deliberation. This prevents misunderstandings about table contents.

Q: Can AutoGen use Alation queries for consensus?

Yes. The agents evaluate the output of listsavedqueries to decide which SQL logic is most appropriate for the task.

Debate data governance with AutoGen and Alation to reach consensus on asset usage.

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Create your Vinkius account to connect Alation 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 Alation with AutoGen

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Debate Alation catalog results in AutoGen

The `search_catalog` tool provides the facts your AutoGen agents use to argue. One agent proposes a query, and the other checks the catalog for accuracy. They reach a consensus based on the actual metadata. This prevents your agents from running queries against deprecated tables.

Audit columns with AutoGen agents

Use `list_columns` to give your agents the schema details they need for their discussion. They verify that the required fields exist before proceeding. This collaborative check happens in real-time. If one agent finds a missing column, the team pivots their strategy immediately.

Resolve governance with Alation and AutoGen

The `list_custom_fields` tool feeds governance rules into your agent conversation. Agents debate whether a specific table meets your security requirements. They only proceed once they agree the data is safe to use. You get a documented decision path from the agent debate.

Setup guide

Set up Alation 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 Alation 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="Alation_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

result = await agent.run("List recent Alation 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="Alation_assistant",
        model_client=OpenAIChatCompletionClient(model="gpt-4o"),
        workbench=workbench,
    )

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

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Common questions about Alation MCP in AutoGen

They pass the results of `list_tables` back and forth during their conversation. This ensures both agents work from the same catalog snapshot.

The agents call `get_object_metadata` to clarify asset details during their deliberation. This prevents misunderstandings about table contents.

Yes. The agents evaluate the output of `list_saved_queries` to decide which SQL logic is most appropriate for the task.

Your agent conversation history captures every tool execution. You review this log to see how the agents interacted with the catalog.

We enforce strict endpoint authentication for all agent requests. The data remains scoped to your specific workspace and is never shared across sessions.

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