How to Use the StarRocks MCP in AutoGen

Q: How does AutoGen help with complex StarRocks queries?

Instead of just running executequery, the agents debate the query structure. They might use gettableschema first, then vote on the best syntax to ensure optimal results.

AutoGen: Get consensus on complex StarRocks architecture decisions through agent debate.

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Connect StarRocks MCP to AutoGen

Create your Vinkius account to connect StarRocks to AutoGen — we handle the hosting, security, and runtime updates so you don't have to. No server setup required.

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Setup StarRocks with AutoGen

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Simulating Architectural Decisions with AutoGen

The core strength here is deliberation. If one agent suggests running a query via `execute_query`, another can challenge it by asking, 'Did you check the schema first?' using `get_table_schema`. This debate forces convergence on the safest and most performant path for complex data retrieval.

Multi-Agent StarRocks Resource Planning

Imagine a 'Security Agent' flagging that certain nodes listed by `list_nodes` are outdated, while a 'Performance Agent' insists on running the query immediately. The negotiation forces you to weigh risk versus speed. This is how AutoGen solves problems that aren't obvious from a single tool call.

Validating StarRocks Data Consistency

You can set up agents to cross-verify data definitions. Agent A runs `list_views` and reports the output. Agent B uses `get_table_schema` on the underlying source tables, forcing a comparison of consistency. This multi-perspective review catches inconsistencies before you write production code.

Setup guide

Set up StarRocks 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 StarRocks 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="StarRocks_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

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

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

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 StarRocks. 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 StarRocks MCP in AutoGen

Instead of just running `execute_query`, the agents debate the query structure. They might use `get_table_schema` first, then vote on the best syntax to ensure optimal results.

Absolutely. Different agents can check different things—one checks `list_nodes`, another runs `get_storage_usage`—and they debate which combination of metrics paints the clearest picture.

You can deploy agents that challenge each other's assumptions about data ownership. They use tools like `list_databases` and `list_views` to build a consensus model of your data landscape.

Yes. The system is built for this exact scenario—handling complex, non-linear workflows where multiple viewpoints must agree on a single action plan.

The server manages structured metadata. This includes records about databases, tables, and views, which are the critical definitions used by your autonomous agents.

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