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 LlamaIndex 7 tools — connect in under 2 minutes

Name: Weaviate
Availability: InStock
Author: Vinkius

docs.llamaindex.ai/en/stable/module_guides/tools/mcp

Built by Vinkius GDPR 7 Tools Framework

LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Weaviate as an MCP tool provider through the Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.

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 llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI

async def main():
    # Your Vinkius token — get it at cloud.vinkius.com
    mcp_client = BasicMCPClient("https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
    mcp_tool_spec = McpToolSpec(client=mcp_client)
    tools = await mcp_tool_spec.to_tool_list_async()

    agent = FunctionAgent(
        tools=tools,
        llm=OpenAI(model="gpt-4o"),
        system_prompt=(
            "You are an assistant with access to Weaviate. "
            "You have 7 tools available."
        ),
    )

    response = await agent.run(
        "What tools are available in Weaviate?"
    )
    print(response)

asyncio.run(main())

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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.

LlamaIndex agents combine Weaviate tool responses with indexed documents for comprehensive, grounded answers. Connect 7 tools through the Vinkius and query live data alongside vector stores and SQL databases in a single turn — ideal for hybrid search, data enrichment, and analytical workflows.

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 LlamaIndex 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 LlamaIndex via MCP

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

Install dependencies

Run pip install llama-index-tools-mcp llama-index-llms-openai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token

Run the agent

Save to agent.py and run: python agent.py

Explore tools

The agent discovers 7 tools from Weaviate

Why Use LlamaIndex with the Weaviate MCP Server

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

Data-first architecture: LlamaIndex agents combine Weaviate tool responses with indexed documents for comprehensive, grounded answers

Query pipeline framework lets you chain Weaviate tool calls with transformations, filters, and re-rankers in a typed pipeline

Multi-source reasoning: agents can query Weaviate, a vector store, and a SQL database in a single turn and synthesize results

Observability integrations show exactly what Weaviate tools were called, what data was returned, and how it influenced the final answer

Weaviate + LlamaIndex Use Cases

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

Hybrid search: combine Weaviate real-time data with embedded document indexes for answers that are both current and comprehensive

Data enrichment: query Weaviate to augment indexed data with live information before generating user-facing responses

Knowledge base agents: build agents that maintain and update knowledge bases by periodically querying Weaviate for fresh data

Analytical workflows: chain Weaviate queries with LlamaIndex's data connectors to build multi-source analytical reports

Weaviate MCP Tools for LlamaIndex (7)

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

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

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

BasicMCPClient not found

Install: pip install llama-index-tools-mcp

Weaviate + LlamaIndex FAQ

Common questions about integrating Weaviate MCP Server with LlamaIndex.

How does LlamaIndex connect to MCP servers?

Use the MCP client adapter to create a connection. LlamaIndex discovers all tools and wraps them as query engine tools compatible with any LlamaIndex agent.

Can I combine MCP tools with vector stores?

Yes. LlamaIndex agents can query Weaviate tools and vector store indexes in the same turn, combining real-time and embedded data for grounded responses.