Can I perform a vector search using float arrays through the agent?

Yes. The `search_near_vector` tool allows you to perform semantic searches by providing a query vector as a JSON array of floats. Your AI agent will return the most similar objects from your Weaviate collection.

How do I see the data structure of my Weaviate collections?

You can use the `get_full_schema` tool to see all classes and properties defined in your instance, or `get_class_schema` if you want to focus on a specific collection's definition.

Is it possible to monitor cluster health via chat?

Absolutely. Use the `get_cluster_nodes` tool to retrieve operational data for all nodes in your Weaviate cluster, including their current status and resource utilization metrics.

Weaviate MCP Server for OpenAI Agents SDK 7 tools — connect in under 2 minutes

Name: Weaviate
Availability: InStock
Author: Vinkius

openai.github.io/openai-agents-python/mcp

Built by Vinkius GDPR 7 Tools SDK

The OpenAI Agents SDK enables production-grade agent workflows in Python. Connect Weaviate through the Vinkius and your agents gain typed, auto-discovered tools with built-in guardrails — no manual schema definitions required.

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

Open in ChatGPT Open in Claude Open in Perplexity

Vinkius supports streamable HTTP and SSE.

python

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

async def main():
    # Your Vinkius token — get it at cloud.vinkius.com
    async with MCPServerStreamableHttp(
        url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    ) as mcp_server:

        agent = Agent(
            name="Weaviate Assistant",
            instructions=(
                "You help users interact with Weaviate. "
                "You have access to 7 tools."
            ),
            mcp_servers=[mcp_server],
        )

        result = await Runner.run(
            agent, "List all available tools from Weaviate"
        )
        print(result.final_output)

asyncio.run(main())

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Stream every event to Splunk, Datadog, or your own webhook in real-time

* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure

About Weaviate MCP Server

Connect your Weaviate instance to any AI agent and harness the power of vector search and semantic data management through natural conversation.

The OpenAI Agents SDK auto-discovers all 7 tools from Weaviate through native MCP integration. Build agents with built-in guardrails, tracing, and handoff patterns — chain multiple agents where one queries Weaviate, another analyzes results, and a third generates reports, all orchestrated through the Vinkius.

What you can do

Semantic Search — Perform nearest neighbor vector similarity searches to find relevant content based on context and meaning
Schema Management — Retrieve the complete instance schema or specific class definitions to understand your data structure
Object Discovery — Browse and list data objects within any class, including full property values and vector data
Deep Data Audit — Retrieve specific data objects by their UUID to inspect metadata and internal configurations
Cluster Monitoring — Monitor operational health, node status, and resource usage of your Weaviate cluster nodes
Instance Metadata — View server version, enabled modules, and high-level configuration details directly from your agent

The Weaviate MCP Server exposes 7 tools through the Vinkius. Connect it to OpenAI Agents SDK in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.

How to Connect Weaviate to OpenAI Agents SDK via MCP

Follow these steps to integrate the Weaviate MCP Server with OpenAI Agents SDK.

Install the SDK

Run pip install openai-agents in your Python environment

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token from cloud.vinkius.com

Run the script

Save the code above and run it: python agent.py

Explore tools

The agent will automatically discover 7 tools from Weaviate

Why Use OpenAI Agents SDK with the Weaviate MCP Server

OpenAI Agents SDK provides unique advantages when paired with Weaviate through the Model Context Protocol.

Native MCP integration via `MCPServerSse` — pass the URL and the SDK auto-discovers all tools with full type safety

Built-in guardrails, tracing, and handoff patterns let you build production-grade agents without reinventing safety infrastructure

Lightweight and composable: chain multiple agents and MCP servers in a single pipeline with minimal boilerplate

First-party OpenAI support ensures optimal compatibility with GPT models for tool calling and structured output

Weaviate + OpenAI Agents SDK Use Cases

Practical scenarios where OpenAI Agents SDK combined with the Weaviate MCP Server delivers measurable value.

Automated workflows: build agents that query Weaviate, process the data, and trigger follow-up actions autonomously

Multi-agent orchestration: create specialist agents — one queries Weaviate, another analyzes results, a third generates reports

Data enrichment pipelines: stream data through Weaviate tools and transform it with OpenAI models in a single async loop

Customer support bots: agents query Weaviate to resolve tickets, look up records, and update statuses without human intervention

Weaviate MCP Tools for OpenAI Agents SDK (7)

These 7 tools become available when you connect Weaviate to OpenAI Agents SDK via MCP:

get_class_schema

Retrieves the schema definition for a specific class (collection)

get_cluster_nodes

Retrieves operational information about the Weaviate cluster nodes

get_full_schema

Retrieves the complete Weaviate schema (all collections)

get_instance_metadata

Retrieves metadata about the Weaviate instance

get_object_details

Retrieves a specific data object by its UUID

list_objects

Supports basic pagination via limit. Lists data objects within a specific class

search_near_vector

Provide a class name and a query vector as a JSON array of floats. Performs a nearest neighbor vector similarity search

Example Prompts for Weaviate in OpenAI Agents SDK

Ready-to-use prompts you can give your OpenAI Agents SDK agent to start working with Weaviate immediately.

"List all classes in my Weaviate schema."

"Search the 'Article' class for items similar to this vector: [0.12, -0.05, 0.88, ...]."

"What is the current health status of my Weaviate cluster nodes?"

Troubleshooting Weaviate MCP Server with OpenAI Agents SDK

Common issues when connecting Weaviate to OpenAI Agents SDK through the Vinkius, and how to resolve them.

MCPServerStreamableHttp not found

Ensure you have the latest version: pip install --upgrade openai-agents

Agent not calling tools

Make sure your prompt explicitly references the task the tools can help with.

Weaviate + OpenAI Agents SDK FAQ

Common questions about integrating Weaviate MCP Server with OpenAI Agents SDK.

How does the OpenAI Agents SDK connect to MCP?

Use MCPServerSse(url=...) to create a server connection. The SDK auto-discovers all tools and makes them available to your agent with full type information.