How to Use the Weaviate MCP in AutoGen

Q: How does AutoGen use Weaviate for vector similarity?

Agents call searchnearvector with their specific query vectors. The results become evidence that the agents must discuss and debate to reach a final, agreed-upon conclusion.

Q: Can AutoGen check the Weaviate schema structure?

Yes. Agents can use getfullschema or getclassschema to verify the available data sources before attempting a multi-step decision process.

Q: How do multiple agents access object details from Weaviate?

Agents call getobjectdetails with specific UUIDs. This action provides concrete, verifiable data that the system uses to resolve conflicting arguments and build consensus.

Q: Does the MCP Server support listing objects for AutoGen?

The listobjects tool supports basic pagination via limit. Agents can use this repeatedly to gather all necessary evidence for their deliberation process.

Drive consensus decisions between multiple agents using AutoGen and Weaviate.

See Vinkius in Action

Works with every AI agent you already use

…and any MCP-compatible client

MCP Servers - Free for Subscribers

Connect Weaviate MCP to AutoGen

Create your Vinkius account to connect Weaviate 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 Weaviate with AutoGen

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Achieve consensus via vector search

`search_near_vector` provides the raw data needed for deliberation. Multiple agents can query this tool simultaneously, passing different vectors to find similar information. The resulting object details are then debated by the multi-agent system until a consensus decision is reached regarding the best course of action.

Validate database structure during debate

Before making a final call, agents need to know what they're working with. `get_full_schema` and `get_class_schema` allow all participating agents to check the complete Weaviate schema. This ensures that every agent is operating on the same understanding of the data model, which prevents conflicting decisions.

Inspect object records for conflict resolution

`get_object_details` lets agents retrieve a specific record by UUID to check facts. One agent might use this tool to verify a piece of information against the source data. The `list_objects` tool allows multiple agents to independently gather lists of objects, presenting conflicting or supporting evidence for the debate.

Setup guide

Set up Weaviate 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 Weaviate 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="Weaviate_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

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

    result = await agent.run("List recent Weaviate 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 Weaviate. 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 Weaviate 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 Weaviate MCP in AutoGen

Agents call `search_near_vector` with their specific query vectors. The results become evidence that the agents must discuss and debate to reach a final, agreed-upon conclusion.

Yes. Agents can use `get_full_schema` or `get_class_schema` to verify the available data sources before attempting a multi-step decision process.

Agents call `get_object_details` with specific UUIDs. This action provides concrete, verifiable data that the system uses to resolve conflicting arguments and build consensus.

The `list_objects` tool supports basic pagination via limit. Agents can use this repeatedly to gather all necessary evidence for their deliberation process.

This server interacts with UUIDs, schema strings, and floating-point vector arrays—all of which serve as core inputs or verifiable outputs during the agent debate cycle.

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