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Weaviate MCP Server

Name: Weaviate
Availability: InStock
Author: Vinkius

weaviate.io

Built by Vinkius GDPR ToolsFree for Subscribers

Search and manage vector data on Weaviate — the AI-native database for building production-grade AI applications.

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What is the Weaviate MCP Server?

The Weaviate MCP Server gives AI agents like Claude, ChatGPT, and Cursor direct access to Weaviate via 7 tools. Search and manage vector data on Weaviate — the AI-native database for building production-grade AI applications. Powered by the Vinkius - no API keys, no infrastructure, connect in under 2 minutes.

Built-in capabilities (7)

get_class_schemaget_cluster_nodesget_full_schemaget_instance_metadataget_object_detailslist_objectssearch_near_vector

Tools for your AI Agents to operate Weaviate

Ask your AI agent "List all classes in my Weaviate schema." and get the answer without opening a single dashboard. With 7 tools connected to real Weaviate data, your agents reason over live information, cross-reference it with other MCP servers, and deliver insights you would spend hours assembling manually.

Works with Claude, ChatGPT, Cursor, and any MCP-compatible client. Powered by the Vinkius - your credentials never touch the AI model, every request is auditable. Connect in under two minutes.

Why teams choose Vinkius

One subscription gives you access to thousands of MCP servers - and you can deploy your own to the Vinkius Edge. Your AI agents only access the data you authorize, with DLP that blocks sensitive information from ever reaching the model, kill switch for instant shutdown, and up to 60% token savings. Enterprise-grade infrastructure and security, zero maintenance.

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Weaviate MCP Server capabilities

7 tools

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

What the Weaviate MCP Server unlocks

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

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

How it works

1. Subscribe to this server
2. Enter your Weaviate Base URL and API Key
3. Start querying your vector data collections through Claude, Cursor, or any MCP-compatible client

No more manual JSON querying in complex database consoles. Your AI agent becomes your vector database administrator.

Who is this for?

AI Developers — test and refine semantic search queries and verify vector data ingestion
Data Engineers — audit database schemas, monitor cluster health, and browse indexed objects
Research Teams — quickly surface relevant documents and data points from massive vector collections through chat
SRE & DevOps — monitor the operational status of Weaviate nodes and manage instance configurations

Frequently asked questions about the Weaviate MCP Server

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.