LanceDB (Serverless Vector DB) Connector for AI agents.
6 live capabilities
Manage cloud-hosted vector storage and RAG infrastructure through natural conversation.
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Why people use LanceDB (Serverless Vector DB)
LanceDB Vector DB Management for RAG Developers
With this Connector, you just tell your agent what you need. You can ask it to list your tables, provision new ones with specific Arrow schemas, or run a KNN search on the fly. You get a direct line to your cloud-hosted vector storage without the overhead of writing extra scripts.
What Vinkius changes
You get a direct conversational interface for your cloud-hosted vector database.
Use it from Claude, ChatGPT, Cursor or another AI client you already have.
One account · 5,900+ Connectors
- Real-world use case 01
Verifying document retrieval
A developer wants to see if a new document chunk was indexed correctly.
- Real-world use case 02
Provisioning new RAG pipelines
A data engineer needs to set up a new RAG pipeline for a client.
- Real-world use case 03
Multi-modal image search
A researcher wants to find similar images in a multi-modal set.
Complete set · 6capabilities
The complete LanceDB (Serverless Vector DB) capability set.
These are the exact actions your AI can choose when you ask it to work with LanceDB (Serverless Vector DB).
01—03
3 capabilities in this set.
Part of 6 available through LanceDB (Serverless Vector DB).
- 01 Capability
List tables
See every vectorized table currently living in your LanceDB instance. This helps you keep track of your active data collections.
- 02 Capability
Get table
Pull the exact schema and metadata for a specific table to check tensor dimensions. It ensures your agent knows the data structure.
- 03 Capability
Vector search
Execute a high-speed KNN similarity search to find semantically related rows. This is the primary way to perform RAG lookups.
04—06
3 capabilities in this set.
Part of 6 available through LanceDB (Serverless Vector DB).
- 04 Capability
Insert rows
Add new structured data and vectors while the system updates the ANN index automatically. It keeps your search results current.
- 05 Capability
Create table
Provision a new vector table with a custom Apache Arrow schema for your specific workload. This ensures strict data integrity.
- 06 Capability
Delete table
Permanently remove a vector table to keep your storage environment clean. Use this to vaporize old test data.
Set up in minutes
One URL. Then ask LanceDB (Serverless Vector DB) to work.
Claude and ChatGPT only need the Connector URL. Copy it once, add it in settings, and use LanceDB (Serverless Vector DB) from the conversation.
Choose your client
Live previewAdvanced clients IDE · CLI
Claude · Web + desktop
Connector URL · ready to paste
Streamable HTTPhttps://edge.vinkius.com/vk_preview_85lSO635ajE8lXHCZKWQ7UC6VHfybBdy20nU97wm/mcp - Step 01
Open Connectors
In Claude Web or Claude Desktop, open Settings and choose Connectors.
- Step 02
Add the URL
Choose Add custom connector, name it LanceDB (Serverless Vector DB), and paste the URL above.
- Step 03
Turn it on in chat
Select +, open Connectors, and enable LanceDB (Serverless Vector DB) for the conversation.
ChatGPT · Web + desktop
Connector URL · ready to paste
Streamable HTTPhttps://edge.vinkius.com/vk_preview_85lSO635ajE8lXHCZKWQ7UC6VHfybBdy20nU97wm/mcp - Step 01
Open MCP settings
On desktop, open Settings and MCP servers. On web, open your workspace app or connector settings.
- Step 02
Add the URL
Choose Add server with Streamable HTTP, or create a custom MCP app, then paste the LanceDB (Serverless Vector DB) URL.
- Step 03
Save and start
Save the connection and enable LanceDB (Serverless Vector DB) in your conversation. Desktop may ask you to restart once.
Cursor · IDE configuration
Advanced setup
{
"mcpServers": {
"lancedb-serverless-vector-db": {
"url": "https://edge.vinkius.com/vk_preview_85lSO635ajE8lXHCZKWQ7UC6VHfybBdy20nU97wm/mcp"
}
}
}- Step 01
Open MCP Settings
Press Cmd+Shift+P (macOS) or Ctrl+Shift+P (Windows/Linux) → search "MCP Settings"
- Step 02
Add the server config
Paste the JSON configuration above into the mcp.json file that opens
- Step 03
Save the file
Cursor will automatically detect the new Connector
- Step 04
Start using LanceDB (Serverless Vector DB)
Open Agent mode in chat and ask: "Using LanceDB (Serverless Vector DB), help me...". 6 tools available
VS Code Copilot · IDE configuration
Advanced setup
{
"mcpServers": {
"lancedb-serverless-vector-db": {
"url": "https://edge.vinkius.com/vk_preview_85lSO635ajE8lXHCZKWQ7UC6VHfybBdy20nU97wm/mcp"
}
}
}- Step 01
Create MCP config
Create a .vscode/mcp.json file in your project root
- Step 02
Add the server config
Paste the JSON configuration above
- Step 03
Enable Agent mode
Open GitHub Copilot Chat and switch to Agent mode using the dropdown
- Step 04
Start using LanceDB (Serverless Vector DB)
Ask Copilot: "Using LanceDB (Serverless Vector DB), help me...". 6 tools available
Windsurf · IDE configuration
Advanced setup
{
"mcpServers": {
"lancedb-serverless-vector-db": {
"url": "https://edge.vinkius.com/vk_preview_85lSO635ajE8lXHCZKWQ7UC6VHfybBdy20nU97wm/mcp"
}
}
}- Step 01
Open MCP Settings
Go to Settings → MCP Configuration or press Cmd+Shift+P and search "MCP"
- Step 02
Add the server
Paste the JSON configuration above into mcp_config.json
- Step 03
Save and reload
Windsurf will detect the new server automatically
- Step 04
Start using LanceDB (Serverless Vector DB)
Open Cascade and ask: "Using LanceDB (Serverless Vector DB), help me...". 6 tools available
Cline · IDE configuration
Advanced setup
{
"mcpServers": {
"lancedb-serverless-vector-db": {
"url": "https://edge.vinkius.com/vk_preview_85lSO635ajE8lXHCZKWQ7UC6VHfybBdy20nU97wm/mcp"
}
}
}- Step 01
Open Cline MCP Settings
Click the Connectors icon in the Cline sidebar panel
- Step 02
Add remote server
Click "Add Connector" and paste the configuration above
- Step 03
Enable the server
Toggle the server switch to ON
- Step 04
Start using LanceDB (Serverless Vector DB)
Ask Cline: "Using LanceDB (Serverless Vector DB), help me...". 6 tools available
Claude Code · Terminal command
Advanced setup
claude mcp add lancedb-serverless-vector-db --transport http "https://edge.vinkius.com/vk_preview_85lSO635ajE8lXHCZKWQ7UC6VHfybBdy20nU97wm/mcp"- Step 01
Install Claude Code
Run npm install -g @anthropic-ai/claude-code if not already installed
- Step 02
Add the Connector
Run the command above in your terminal
- Step 03
Verify the connection
Run claude mcp to list connected servers, or type /mcp inside a session
- Step 04
Start using LanceDB (Serverless Vector DB)
Ask Claude: "Using LanceDB (Serverless Vector DB), show me...". 6 tools are ready
Where the request belongs
Work LanceDB can move forward.
This is for RAG developers who are tired of writing boilerplate code for every search, data engineers who need to manage strict schemas, and AI architects who need to audit storage across multiple instances.
RAG Developer
You use this on a Tuesday afternoon to quickly verify if your new document chunks are being retrieved correctly without opening a terminal.
Data Engineer
You use this to provision multiple vector tables with specific dimensions to support different multi-modal AI models.
AI Architect
You use this to audit your cloud storage usage and verify vector topologies across various production environments.
Build the capability set
Add more capabilities.
Each Connector adds new actions and data without changing how you work.
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Qdrant
Empower your AI to interact directly with your Qdrant vector database. query clusters, perform similarity searches, and manage collections effortlessly.
Couchbase (Vector & NoSQL)
Manage vector search and NoSQL via Couchbase. execute N1QL queries, perform KNN vector searches, and audit documents directly from any AI agent.
Milvus (Open-Source Vector Database)
Manage vector storage via Milvus. perform ANN searches, query scalar entities, and audit collections.
MongoDB Atlas Vector Search
Manage vector storage via MongoDB Atlas. perform similarity searches, query MQL documents, and audit collections.
Bring your own AI
Change the model, client or framework. Keep LanceDB connected.
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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 LanceDB.
The practical details behind the request, access and result.
What does the LanceDB MCP do for my RAG system?
It gives your AI agent the ability to interact directly with your vector storage. You can ask it to perform similarity searches, check table schemas, or manage your data without writing any extra code.
Can I use LanceDB MCP to search through my embeddings?
Yes, you can perform high-speed KNN similarity searches. Just provide the vector to your agent, and it will find the most relevant rows in your LanceDB instance.
How does the LanceDB MCP handle new data?
When you insert new rows, the Connector ensures the underlying ANN index is updated in real time. This means your agent always sees the most current information.
Can I create new tables using the LanceDB MCP?
Absolutely. You can ask your agent to provision new vector tables with specific Apache Arrow schemas to ensure your data remains consistent and organized.
How do I manage my LanceDB Cloud storage with this Connector?
You can manage your storage by listing all active tables, verifying their configurations, and deleting old tables to keep your environment clean and optimized.
Does the LanceDB MCP support multi-modal data?
Yes, it is designed to handle multi-modal embeddings. You can manage the different topologies and schemas required for complex AI workloads through natural conversation.
Can I perform a semantic similarity search using my agent?
Yes. Use the vector_search capability by providing the target Table name and a JSON array of floating-point numbers representing your query embedding. Your agent will return the k-nearest rows from LanceDB based on semantic similarity.
How do I create a new table with a specific Apache Arrow schema?
The create_table capability allows your agent to initialize a new columnar vector table. You just need to provide the desired Table name and a valid Apache Arrow schema mapping in JSON format defining dimensions and scalar fields.
Can my agent insert new embeddings directly into a LanceDB table?
Absolutely. Use the insert_rows capability to persist new data rows containing native embeddings and arbitrary JSON metadata. Your agent will handle the payload delivery, and LanceDB will automatically update its ANN index.
One connection away
Give your agent a direct line to LanceDB.
Connect LanceDB once. Keep it beside 5,900+ managed Connectors when the next task needs more.
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