Can I perform a semantic similarity search using my agent?

Yes. Use the `vector_search` tool 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` tool 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` tool 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.

LanceDB (Serverless Vector DB) MCP Server for OpenAI Agents SDK 6 tools — connect in under 2 minutes

Name: LanceDB (Serverless Vector DB)
Availability: InStock
Author: Vinkius

openai.github.io/openai-agents-python/mcp

Built by Vinkius GDPR 6 Tools SDK

The OpenAI Agents SDK enables production-grade agent workflows in Python. Connect LanceDB (Serverless Vector DB) through the Vinkius and your agents gain typed, auto-discovered tools with built-in guardrails — no manual schema definitions required.

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 agents import Agent, Runner
from agents.mcp import MCPServerStreamableHttp

async def main():
    # Your Vinkius token — get it at cloud.vinkius.com
    async with MCPServerStreamableHttp(
        url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    ) as mcp_server:

        agent = Agent(
            name="LanceDB (Serverless Vector DB) Assistant",
            instructions=(
                "You help users interact with LanceDB (Serverless Vector DB). "
                "You have access to 6 tools."
            ),
            mcp_servers=[mcp_server],
        )

        result = await Runner.run(
            agent, "List all available tools from LanceDB (Serverless Vector DB)"
        )
        print(result.final_output)

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 LanceDB (Serverless Vector DB) MCP Server

Connect your LanceDB Cloud account to any AI agent and take full control of your serverless vector storage and RAG infrastructure through natural conversation.

The OpenAI Agents SDK auto-discovers all 6 tools from LanceDB (Serverless Vector DB) through native MCP integration. Build agents with built-in guardrails, tracing, and handoff patterns — chain multiple agents where one queries LanceDB (Serverless Vector DB), another analyzes results, and a third generates reports, all orchestrated through the Vinkius.

What you can do

Vector Orchestration — List all vectorized tables and retrieve precise schema metadata, including tensor dimensions and vector topologies directly from your agent
Similarity Search — Execute highly-optimized KNN (K-Nearest Neighbor) lookups to retrieve semantically related rows based on embedding array similarity
Dynamic Ingestion — Insert new structured row payloads and vectors into existing tables, updating the underlying ANN index in real-time
Table Management — Provision new columnar vector tables declaring specific Apache Arrow schemas and multi-dimensional layouts required for AI workloads
Database Audit — Discover active table boundaries and verify storage configurations assigned to your serverless database instance securely
Resource Cleanup — Irreversibly delete entire vector tables to maintain a clean and optimized data environment for your AI applications

The LanceDB (Serverless Vector DB) MCP Server exposes 6 tools through the Vinkius. Connect it to OpenAI Agents SDK 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 LanceDB (Serverless Vector DB) to OpenAI Agents SDK via MCP

Follow these steps to integrate the LanceDB (Serverless Vector DB) MCP Server with OpenAI Agents SDK.

Install the SDK

Run pip install openai-agents in your Python environment

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token from cloud.vinkius.com

Run the script

Save the code above and run it: python agent.py

Explore tools

The agent will automatically discover 6 tools from LanceDB (Serverless Vector DB)

Why Use OpenAI Agents SDK with the LanceDB (Serverless Vector DB) MCP Server

OpenAI Agents SDK provides unique advantages when paired with LanceDB (Serverless Vector DB) through the Model Context Protocol.

Native MCP integration via `MCPServerSse` — pass the URL and the SDK auto-discovers all tools with full type safety

Built-in guardrails, tracing, and handoff patterns let you build production-grade agents without reinventing safety infrastructure

Lightweight and composable: chain multiple agents and MCP servers in a single pipeline with minimal boilerplate

First-party OpenAI support ensures optimal compatibility with GPT models for tool calling and structured output

LanceDB (Serverless Vector DB) + OpenAI Agents SDK Use Cases

Practical scenarios where OpenAI Agents SDK combined with the LanceDB (Serverless Vector DB) MCP Server delivers measurable value.

Automated workflows: build agents that query LanceDB (Serverless Vector DB), process the data, and trigger follow-up actions autonomously

Multi-agent orchestration: create specialist agents — one queries LanceDB (Serverless Vector DB), another analyzes results, a third generates reports

Data enrichment pipelines: stream data through LanceDB (Serverless Vector DB) tools and transform it with OpenAI models in a single async loop

Customer support bots: agents query LanceDB (Serverless Vector DB) to resolve tickets, look up records, and update statuses without human intervention

LanceDB (Serverless Vector DB) MCP Tools for OpenAI Agents SDK (6)

These 6 tools become available when you connect LanceDB (Serverless Vector DB) to OpenAI Agents SDK via MCP:

create_table

Provision a new LanceDB table with a strict schema

delete_table

Irreversibly vaporize an entire LanceDB vector table

get_table

Get precise schema and metadata for a specific LanceDB table

insert_rows

Data dynamically updates the underlying ANN index. Insert structured row payloads and vectors into a table

list_tables

List all vectorized tables residing in LanceDB

vector_search

Perform a highly-optimized KNN Vector similarity search

Example Prompts for LanceDB (Serverless Vector DB) in OpenAI Agents SDK

Ready-to-use prompts you can give your OpenAI Agents SDK agent to start working with LanceDB (Serverless Vector DB) immediately.

"List all active tables in my LanceDB instance"

"Perform a vector search in 'product_embeddings' for this vector: [0.1, 0.2, ...]"

"Show me the schema for the 'support_kb' table"

Troubleshooting LanceDB (Serverless Vector DB) MCP Server with OpenAI Agents SDK

Common issues when connecting LanceDB (Serverless Vector DB) to OpenAI Agents SDK through the Vinkius, and how to resolve them.

MCPServerStreamableHttp not found

Ensure you have the latest version: pip install --upgrade openai-agents

Agent not calling tools

Make sure your prompt explicitly references the task the tools can help with.

LanceDB (Serverless Vector DB) + OpenAI Agents SDK FAQ

Common questions about integrating LanceDB (Serverless Vector DB) MCP Server with OpenAI Agents SDK.

How does the OpenAI Agents SDK connect to MCP?

Use MCPServerSse(url=...) to create a server connection. The SDK auto-discovers all tools and makes them available to your agent with full type information.