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 LangChain 7 tools — connect in under 2 minutes

Name: Weaviate
Availability: InStock
Author: Vinkius

python.langchain.com/docs/integrations/tools/mcp

Built by Vinkius GDPR 7 Tools Framework

LangChain is the leading Python framework for composable LLM applications. Connect Weaviate through the Vinkius and LangChain agents can call every tool natively — combine them with retrievers, memory, and output parsers for sophisticated AI pipelines.

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 langchain_mcp_adapters.client import MultiServerMCPClient
from langchain_openai import ChatOpenAI
from langgraph.prebuilt import create_react_agent

async def main():
    # Your Vinkius token — get it at cloud.vinkius.com
    async with MultiServerMCPClient({
        "weaviate": {
            "transport": "streamable_http",
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp",
        }
    }) as client:
        tools = client.get_tools()
        agent = create_react_agent(
            ChatOpenAI(model="gpt-4o"),
            tools,
        )
        response = await agent.ainvoke({
            "messages": [{
                "role": "user",
                "content": "Using Weaviate, show me what tools are available.",
            }]
        })
        print(response["messages"][-1].content)

asyncio.run(main())

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* 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.

LangChain's ecosystem of 500+ components combines seamlessly with Weaviate through native MCP adapters. Connect 7 tools via the Vinkius and use ReAct agents, Plan-and-Execute strategies, or custom agent architectures — with LangSmith tracing giving full visibility into every tool call, latency, and token cost.

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 LangChain 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 LangChain via MCP

Follow these steps to integrate the Weaviate MCP Server with LangChain.

Install dependencies

Run pip install langchain langchain-mcp-adapters langgraph langchain-openai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token

Run the agent

Save the code and run python agent.py

Explore tools

The agent discovers 7 tools from Weaviate via MCP

Why Use LangChain with the Weaviate MCP Server

LangChain provides unique advantages when paired with Weaviate through the Model Context Protocol.

The largest ecosystem of integrations, chains, and agents — combine Weaviate MCP tools with 500+ LangChain components

Agent architecture supports ReAct, Plan-and-Execute, and custom strategies with full MCP tool access at every step

LangSmith tracing gives you complete visibility into tool calls, latencies, and token usage for production debugging

Memory and conversation persistence let agents maintain context across Weaviate queries for multi-turn workflows

Weaviate + LangChain Use Cases

Practical scenarios where LangChain combined with the Weaviate MCP Server delivers measurable value.

RAG with live data: combine Weaviate tool results with vector store retrievals for answers grounded in both real-time and historical data

Autonomous research agents: LangChain agents query Weaviate, synthesize findings, and generate comprehensive research reports

Multi-tool orchestration: chain Weaviate tools with web scrapers, databases, and calculators in a single agent run

Production monitoring: use LangSmith to trace every Weaviate tool call, measure latency, and optimize your agent's performance

Weaviate MCP Tools for LangChain (7)

These 7 tools become available when you connect Weaviate to LangChain 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 LangChain

Ready-to-use prompts you can give your LangChain 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 LangChain

Common issues when connecting Weaviate to LangChain through the Vinkius, and how to resolve them.

MultiServerMCPClient not found

Install: pip install langchain-mcp-adapters

Weaviate + LangChain FAQ

Common questions about integrating Weaviate MCP Server with LangChain.

How does LangChain connect to MCP servers?

Use langchain-mcp-adapters to create an MCP client. LangChain discovers all tools and wraps them as native LangChain tools compatible with any agent type.

Which LangChain agent types work with MCP?

All agent types including ReAct, OpenAI Functions, and custom agents work with MCP tools. The tools appear as standard LangChain tools after the adapter wraps them.