How to Use the LanceDB (Serverless Vector DB) MCP in LlamaIndex

Q: Can I index the output of LanceDB (Serverless Vector DB) in LlamaIndex?

Absolutely. When your agent runs a vectorsearch, LlamaIndex can store those KNN results in its own semantic index for future reference.

Q: How do I restrict which tables the agent can delete?

You use LlamaIndex's allowedtools filter. Simply exclude deletetable from the tool specification before handing it to the agent.

Ground your LlamaIndex RAG applications with live LanceDB vector searches.

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Connect LanceDB (Serverless Vector DB) MCP to LlamaIndex

Create your Vinkius account to connect LanceDB (Serverless Vector DB) to LlamaIndex and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

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Setup LanceDB (Serverless Vector DB) with LlamaIndex

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LlamaIndex MCP Server Retrieval

Standard RAG relies on static documents. By attaching this MCP Server to LlamaIndex, your FunctionAgent can execute a live `vector_search` against your LanceDB tables during a user query. The agent pulls the exact multi-modal embeddings it needs at runtime. Those KNN results do not just disappear after the prompt. LlamaIndex ingests the tool output, allowing you to query past retrieval sessions. You get answers grounded in actual database records rather than hallucinated approximations.

Live Index Updating

Your data pipelines move fast. When new documents arrive, your LlamaIndex application calls `create_table` to provision a fresh schema, then pushes the vectorized chunks using `insert_rows`. The underlying ANN index updates dynamically. Manual database administration becomes obsolete. The agent handles the ingestion directly through the MCP protocol, ensuring your searchable knowledge base stays current with zero human intervention.

Metadata Verification

Before running a complex query, your application needs to know the data structure. The agent fires `list_tables` to find the right collection and `get_table` to read the exact metadata. This prevents malformed queries from crashing your application. If a table is obsolete or heavily fragmented, the agent can trigger `delete_table` to clear out the old vectors.

Setup guide

Set up LanceDB (Serverless Vector DB) MCP in LlamaIndex

Prerequisites

Python 3.10+ installed
llama-index-tools-mcp package
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai. The MCP tools package provides BasicMCPClient and McpToolSpec.
2

Connect with BasicMCPClient
Point BasicMCPClient to your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports SSE and Streamable HTTP transports.
3

Convert to LlamaIndex tools
Call mcp_tool_spec.to_tool_list_async() to convert all LanceDB (Serverless Vector DB) MCP tools into native FunctionTool objects that any LlamaIndex agent can use.
4

Run with any LLM
Create a FunctionAgent with the tools and your preferred LLM. Swap OpenAI for Anthropic, Gemini, or any LlamaIndex-supported provider.

agent.py

from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI

# Connect to the MCP
mcp_client = BasicMCPClient(
    "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)
mcp_tool_spec = McpToolSpec(client=mcp_client)

# Convert MCP tools to LlamaIndex tools
tools = await mcp_tool_spec.to_tool_list_async()

# Create and run the agent
agent = FunctionAgent(
    tools=tools,
    llm=OpenAI(model="gpt-4o"),
    system_prompt="You have access to LanceDB (Serverless Vector DB) tools.",
)
response = await agent.run("List recent LanceDB (Serverless Vector DB) data")

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Common questions about LanceDB (Serverless Vector DB) MCP in LlamaIndex

You install llama-index-tools-mcp and initialize a BasicMCPClient. Pass the resulting tools to your FunctionAgent so it can execute searches.

Absolutely. When your agent runs a `vector_search`, LlamaIndex can store those KNN results in its own semantic index for future reference.

Yes. The underlying database handles multi-modal embeddings natively. Your agent just needs to pass the correct float arrays to `insert_rows`.

You use LlamaIndex's allowed_tools filter. Simply exclude `delete_table` from the tool specification before handing it to the agent.

This integration routes highly-optimized KNN search queries and float arrays straight to your database. Your proprietary embedding representations never touch public logging endpoints or unverified proxy servers.

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