How to Use the MyScale (SQL Vector Database API) MCP in AutoGen

Q: Can AutoGen build and monitor index states in MyScale (SQL Vector Database API) autonomously via an MCP Server?

Yes, your agents can run createvectorindex to build a new index on your table. They can then poll checkindexstatus to determine exactly when the index is ready to handle search queries.

Q: How do AutoGen agents handle database offline errors with MyScale (SQL Vector Database API)?

If pingcluster returns an error, your supervisor agent can pause the workflow. The agents can then discuss alternative strategies or wait and retry the connection before running any more SQL commands.

Q: What happens if an agent writes an invalid vector search query?

The vectorsearch tool returns the raw database error to the executing agent. In AutoGen, this agent can hand the error message to a developer agent, which corrects the SQL parameters and runs the search again.

Let AutoGen agents debate query strategies and execute vector searches on MyScale (SQL Vector Database API).

See Vinkius in Action

Works with every AI agent you already use

…and any MCP-compatible client

MCP Servers - Free for Subscribers

Connect MyScale (SQL Vector Database API) MCP to AutoGen

Create your Vinkius account to connect MyScale (SQL Vector Database API) 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.

GDPR Free for Subscribers

Setup MyScale (SQL Vector Database API) with AutoGen

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Multi-Agent Consensus on SQL Query Design

In an AutoGen setup, an executor agent uses `execute_sql_query` to fetch records once the analyst and security agents agree on the query parameters. This collaborative approach prevents poorly optimized queries from hitting your system. By separating query design from database execution, your agents safely search large datasets without risking performance degradation.

Deploy This MCP Server for Autonomous Index Management

Your AutoGen agents manage the database lifecycle autonomously, starting with `create_vector_table` over this MCP connection. One agent creates tables with `create_vector_table`, while another configures search settings using `create_vector_index`. A coordinator agent then runs `check_index_status` in a loop to verify when the index is ready. This allows your multi-agent system to ingest raw text, convert it to vectors, and index it entirely in the background.

Collaborative Database Health and Search Verification

Before executing complex queries, an agent verifies system availability using `ping_cluster`. When the cluster is online, they execute similarity searches via `vector_search` through the MCP client. The agents then analyze the returned similarity scores to decide if they need to run a broader SQL query to find better matches.

Setup guide

Set up MyScale (SQL Vector Database API) 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 MyScale (SQL Vector Database API) 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="MyScale (SQL Vector Database API)_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

result = await agent.run("List recent MyScale (SQL Vector Database API) data")
print(result.messages[-1].content)

Get your connection token →

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="MyScale (SQL Vector Database API)_assistant",
        model_client=OpenAIChatCompletionClient(model="gpt-4o"),
        workbench=workbench,
    )

    result = await agent.run("List recent MyScale (SQL Vector Database API) 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 MyScale. 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.

Why Choose Vinkius

Vinkius connects your tools to AI with real-time monitoring and automatic cost savings — all from one dashboard.

Connect MyScale (SQL Vector Database API) now

Real-time monitoring

Live

visibility into every interaction

Connect your favorite tools to your AI and see exactly what's happening — every request, every response, in real time.

Built-in savings

60%

lower AI costs

Vinkius compresses data between your apps and your AI automatically. Lower bills every month — no configuration required.

Single dashboard

One

place for every integration

Every tool your AI connects to, managed from a single screen. One account, complete control.

Common questions about MyScale (SQL Vector Database API) MCP in AutoGen

One agent can write the SQL query, while a second agent executes it using `execute_sql_query`. A third agent can then process the returned data to determine if the results meet your search criteria.

Yes, your agents can run `create_vector_index` to build a new index on your table. They can then poll `check_index_status` to determine exactly when the index is ready to handle search queries.

If `ping_cluster` returns an error, your supervisor agent can pause the workflow. The agents can then discuss alternative strategies or wait and retry the connection before running any more SQL commands.

The `vector_search` tool returns the raw database error to the executing agent. In AutoGen, this agent can hand the error message to a developer agent, which corrects the SQL parameters and runs the search again.

All tool execution runs within an ephemeral V8 sandbox managed by the Vinkius MCP host. Your SQL queries, vector embeddings, and database schema info are processed in isolation, ensuring your proprietary data never leaks to other tenants.

Use it with your favorite AI tools

Connect this server to Cursor, Claude, VS Code, and more.

OpenAI Agents SDK sdk-python

Google ADK sdk-python

Pydantic AI sdk-python

Vercel AI SDK sdk-typescript