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Vinkius

Weaviate Connector for AI agents.

7 live capabilities

Query vector database collections and manage semantic data.

Live agent request Weaviate / Connector

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AI Agent

Why people use Weaviate

Weaviate for Vector Database Schema Auditing

This Connector removes that friction by making your AI client the primary interface. You can ask your agent to pull specific object details or list everything in a collection without ever touching a console. It turns your chat window into a live dashboard for your vector data.

  • Claude
  • ChatGPT
  • Gemini
  • Cursor
  • Visual Studio Code
  • Windsurf

What Vinkius changes

You get a conversational interface for your vector database without the manual overhead of complex queries.

Use it from Claude, ChatGPT, Cursor or another AI client you already have.

One account · 5,900+ Connectors

  1. Real-world use case 01

    Verifying successful data ingestion

    A developer needs to see if a specific record was ingested correctly.

  2. Real-world use case 02

    Checking infrastructure health

    An SRE notices a lag in search results.

  3. Real-world use case 03

    Exploring new collections

    A researcher wants to see what's in a new collection.

Complete set · 7capabilities

The complete Weaviate capability set.

These are the exact actions your AI can choose when you ask it to work with Weaviate.

Capability set01 / 02

01—04

4 capabilities in this set.

Part of 7 available through Weaviate.

  1. 01 Capability

    Get cluster nodes

    Retrieve operational status and resource usage for your cluster nodes. It provides a quick health check for your infrastructure.

  2. 02 Capability

    Get object details

    Pull all metadata and properties for a specific object using its UUID. This is great for deep auditing of individual records.

  3. 03 Capability

    Get full schema

    Fetch the complete schema for every collection in your Weaviate instance. Use this to get a bird's-eye view of your entire data structure.

  4. 04 Capability

    List objects

    Browse and list data objects within a specific class. It includes basic pagination to help you explore your collections.

Capability set02 / 02

05—07

3 capabilities in this set.

Part of 7 available through Weaviate.

  1. 05 Capability

    Search near vector

    Perform a nearest neighbor similarity search using a provided class name and vector array. This is how you find contextually relevant data.

  2. 06 Capability

    Get class schema

    Get the specific schema definition for a single collection class. This helps you confirm what properties are available for your queries.

  3. 07 Capability

    Get instance metadata

    View high-level metadata about your current Weaviate instance. Use this to check your active modules and version details.

Set up in minutes

One URL. Then ask Weaviate to work.

Claude and ChatGPT only need the Connector URL. Copy it once, add it in settings, and use Weaviate from the conversation.

Choose your client

Live preview
Advanced clients IDE · CLI

Claude · Web + desktop

Official guide ↗

Connector URL · ready to paste

Streamable HTTP
https://edge.vinkius.com/vk_preview_ewBpKaEj3GWOCB4HdPc1Tag8b8QBrbGJDpCpFWfW/mcp
  1. Step 01

    Open Connectors

    In Claude Web or Claude Desktop, open Settings and choose Connectors.

  2. Step 02

    Add the URL

    Choose Add custom connector, name it Weaviate, and paste the URL above.

  3. Step 03

    Turn it on in chat

    Select +, open Connectors, and enable Weaviate for the conversation.

Where the request belongs

Work Weaviate can move forward.

Built around the request

This is for the AI engineer who is tired of manual data auditing and the DevOps person who needs to keep an eye on cluster health without constant tab switching.

01

AI Developer

Testing vector search accuracy and verifying data ingestion during development.

02

Data Engineer

Auditing schemas and browsing indexed objects to ensure data integrity.

03

SRE / DevOps

Monitoring node health and managing instance configurations across the cluster.

When one Connector is not enough

Combine Weaviate with the systems that finish the task.

View all recipes
01

Find Codebase Duplications Using Connectors

Your codebase has 4 different implementations of date formatting, 3 versions of the retry logic, and 2 competing validation libraries , but nobody knows because grep only finds exact matches and these duplicates are semantic

 02

Improve RAG Search Quality Using Connectors

Your RAG retrieves 10 documents but the answer is in #7 , Cohere reranking moves it to #1 and accuracy jumps from 68% to 94% without changing a single embedding

 03

Connectors for Self-Updating Research Bases

You spend 3 hours reading 40 articles to write one research brief , an AI agent with Firecrawl reads all 40 in 90 seconds, stores them semantically in Weaviate, and writes the brief in Notion with every source linked and every claim verified

 04

Search Your Entire Codebase Using Connectors

Code indexed, patterns detected, architecture documented, onboarding guides generated , build a living knowledge base from your codebase

Build the capability set

Add more capabilities.

Each Connector adds new actions and data without changing how you work.

Browse Connectors
Qdrant logo
01 7 capabilities

Qdrant

Empower your AI to interact directly with your Qdrant vector database. query clusters, perform similarity searches, and manage collections effortlessly.

View Connector
Milvus (Open-Source Vector Database) logo
02 7 capabilities

Milvus (Open-Source Vector Database)

Manage vector storage via Milvus. perform ANN searches, query scalar entities, and audit collections.

View Connector
Supabase Vector logo
03 7 capabilities

Supabase Vector

Connect your AI to Supabase Vector. Execute pgvector semantic searches, manage embeddings, and run relational database queries directly from your terminal.

View Connector
Elasticsearch Vector logo
04 6 capabilities

Elasticsearch Vector

Empower vector search via Elasticsearch. perform dense vector kNN searches, handle index mappings, and index embedding documents directly from any AI agent.

View Connector
Vertex AI Vector Search logo
05 6 capabilities

Vertex AI Vector Search

Bring Google's massive vector matching power to your AI agent. Search billions of semantic embeddings and administer Vertex Index endpoints directly in chat.

View Connector
OpenSearch Vector logo
06 6 capabilities

OpenSearch Vector

Run k-NN vector searches on OpenSearch. create indexes, upsert embeddings, query similar documents, and manage your vector store from any AI agent.

View Connector

Bring your own AI

Change the model, client or framework. Keep Weaviate connected.

  • Claude
  • ChatGPT
  • Gemini
  • Cursor
  • VS Code
  • Windsurf
  • ZCode
  • Cline
  • Zed
  • Continue
  • Kiro
  • Roo Code
  • Zencoder
  • Goose
  • Void
  • Augment Code
  • Amp
  • Qodo
  • Tabnine
  • Pieces
  • Sourcegraph Cody
  • JetBrains
  • Warp
  • Amazon Q
  • Antigravity
  • BoltAI
  • Raycast
  • Jan
  • LM Studio
  • AnythingLLM
  • Open WebUI
  • Msty
  • Cherry Studio
  • LibreChat
  • TypingMind
  • Chorus
  • 5ire
  • n8n
  • LangChain
  • LlamaIndex
  • CrewAI
  • Vercel AI SDK

Before you connect

Questions about Weaviate.

The practical details behind the request, access and result.

Can I use Weaviate MCP to find similar items in my database?

Yes. You can ask your agent to find content similar to a specific concept or a provided vector. It uses the similarity search capability to pull the most relevant results for you.

How do I check my cluster health using the Weaviate MCP?

Simply ask your agent for a health report. It will automatically check your cluster nodes and summarize the CPU, RAM, and status of your infrastructure.

Can the Weaviate MCP show me my full database schema?

Yes, it can pull the entire schema for all your collections. This is helpful for getting a clear overview of your data structure in one go.

Is the Weaviate MCP good for auditing specific records?

It's excellent for auditing. You can provide a UUID to your agent and it will pull every piece of metadata and property associated with that specific object.

Can I use the Weaviate MCP to see my current Weaviate version?

Yes. You can ask for instance metadata, and the agent will report back the version, active modules, and high-level configuration details.

Does the Weaviate MCP support searching by vector embeddings?

Yes, that is a core feature. You can provide a vector array to your agent, and it will perform a nearest neighbor search to find the best matches.

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

Yes. The search_near_vector capability 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 capability 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 capability to retrieve operational data for all nodes in your Weaviate cluster, including their current status and resource utilization metrics.

One connection away

Give your agent a direct line to Weaviate.

Connect Weaviate once. Keep it beside 5,900+ managed Connectors when the next task needs more.

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