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 AutoGen 6 tools — connect in under 2 minutes

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

microsoft.github.io/autogen/docs/tools/mcp-tools

Built by Vinkius GDPR 6 Tools Framework

Microsoft AutoGen enables multi-agent conversations where agents negotiate, delegate, and execute tasks collaboratively. Add LanceDB (Serverless Vector DB) as an MCP tool provider through Vinkius and every agent in the group can access live data and take action.

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 autogen_agentchat.agents import AssistantAgent
from autogen_ext.tools.mcp import McpWorkbench

async def main():
    # Your Vinkius token. get it at cloud.vinkius.com
    async with McpWorkbench(
        server_params={"url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"},
        transport="streamable_http",
    ) as workbench:
        tools = await workbench.list_tools()
        agent = AssistantAgent(
            name="lancedb_serverless_vector_db_agent",
            tools=tools,
            system_message=(
                "You help users with LanceDB (Serverless Vector DB). "
                "6 tools available."
            ),
        )
        print(f"Agent ready with {len(tools)} tools")

asyncio.run(main())

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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.

AutoGen enables multi-agent conversations where agents negotiate, delegate, and collaboratively use LanceDB (Serverless Vector DB) tools. Connect 6 tools through Vinkius and assign role-based access. a data analyst queries while a reviewer validates, with optional human-in-the-loop approval for sensitive operations.

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 AutoGen 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 AutoGen via MCP

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

Install AutoGen

Run pip install "autogen-ext[mcp]"

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token

Integrate into workflow

Use the agent in your AutoGen multi-agent orchestration

Explore tools

The workbench discovers 6 tools from LanceDB (Serverless Vector DB) automatically

Why Use AutoGen with the LanceDB (Serverless Vector DB) MCP Server

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

Multi-agent conversations: multiple AutoGen agents discuss, delegate, and collaboratively use LanceDB (Serverless Vector DB) tools to solve complex tasks

Role-based architecture lets you assign LanceDB (Serverless Vector DB) tool access to specific agents. a data analyst queries while a reviewer validates

Human-in-the-loop support: agents can pause for human approval before executing sensitive LanceDB (Serverless Vector DB) tool calls

Code execution sandbox: AutoGen agents can write and run code that processes LanceDB (Serverless Vector DB) tool responses in an isolated environment

LanceDB (Serverless Vector DB) + AutoGen Use Cases

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

Collaborative analysis: one agent queries LanceDB (Serverless Vector DB) while another validates results and a third generates the final report

Automated review pipelines: a researcher agent fetches data from LanceDB (Serverless Vector DB), a critic agent evaluates quality, and a writer produces the output

Interactive planning: agents negotiate task allocation using LanceDB (Serverless Vector DB) data to make informed decisions about resource distribution

Code generation with live data: an AutoGen coder agent writes scripts that process LanceDB (Serverless Vector DB) responses in a sandboxed execution environment

LanceDB (Serverless Vector DB) MCP Tools for AutoGen (6)

These 6 tools become available when you connect LanceDB (Serverless Vector DB) to AutoGen 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 AutoGen

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

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

McpWorkbench not found

Install: pip install "autogen-ext[mcp]"

LanceDB (Serverless Vector DB) + AutoGen FAQ

Common questions about integrating LanceDB (Serverless Vector DB) MCP Server with AutoGen.

How does AutoGen connect to MCP servers?

Create an MCP tool adapter and assign it to one or more agents in the group chat. AutoGen agents can then call LanceDB (Serverless Vector DB) tools during their conversation turns.

Can different agents have different MCP tool access?

Yes. AutoGen's role-based architecture lets you assign specific MCP tools to specific agents, so a querying agent has different capabilities than a reviewing agent.