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

Build resilient search workflows in Mastra AI with the LanceDB (Serverless Vector DB) MCP Server.

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Connect LanceDB (Serverless Vector DB) MCP to Mastra AI

Create your Vinkius account to connect LanceDB (Serverless Vector DB) to Mastra AI 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 Mastra AI

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Claude

Perplexity

Automated workflow integration

Integrate `vector_search` into your agentic loops to pull context during complex tasks. You can chain these calls within your Mastra AI workflows. If a search fails, you can set up retry logic to handle the transient state. It keeps your multi-step processes moving forward.

Precise table orchestration

Use `create_table` and `delete_table` to manage your database lifecycle as part of a workflow step. You can spin up temporary storage for specific agent tasks. This is useful for cleaning up after a run. Your Mastra AI agent handles the teardown automatically.

Structured data insertion

Trigger `insert_rows` to feed your agents' findings back into the database. This creates a persistent memory for your Mastra AI agents. The data is indexed immediately. You get consistent results across different agent runs.

Setup guide

Set up LanceDB (Serverless Vector DB) MCP in Mastra AI

Prerequisites

Node.js 18+ and a TypeScript project
@mastra/mcp + @mastra/core packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run npm install @mastra/mcp @mastra/core plus your preferred model provider (e.g. @ai-sdk/openai).
2

Configure the MCPClient
Create an MCPClient with your Vinkius endpoint as a URL object. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Discover and inject tools
Call mcpClient.listTools() and spread the result into your agent's tools object. All LanceDB (Serverless Vector DB) tools become native Mastra tools.
4

Run with any model
Swap openai("gpt-4o") for any AI SDK-compatible provider. Call agent.generate() and the agent routes tool calls through MCP automatically.

agent.ts

import { MCPClient } from "@mastra/mcp";
import { Agent } from "@mastra/core/agent";
import { openai } from "@ai-sdk/openai";

const mcpClient = new MCPClient({
  id: "lancedb-serverless-vector-db-mcp-client",
  servers: {
    "lancedb-serverless-vector-db-mcp": {
      url: new URL(
        "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
      ),
    },
  },
});

const agent = new Agent({
  name: "LanceDB (Serverless Vector DB) Agent",
  model: openai("gpt-4o"),
  instructions: "You have access to LanceDB (Serverless Vector DB) tools.",
  tools: {
    ...(await mcpClient.listTools()),
  },
});

const result = await agent.generate(
  "List recent LanceDB (Serverless Vector DB) transactions"
);
console.log(result.text);

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Common questions about LanceDB (Serverless Vector DB) MCP in Mastra AI

You instantiate the MCP client and spread the tools into your agent definition. This allows your agent to call `vector_search` when it needs specific context.

You can wrap these tools in your workflow's retry mechanism. Mastra AI will catch the error and execute your custom recovery logic.

Absolutely. Multiple agents can read from and write to the same tables using the provided tools, ensuring everyone has access to the latest embeddings.

Your vector data is encrypted in transit and restricted to your specific endpoint. Only your authenticated agents have the keys to query the tables.

Yes. You can enable human-in-the-loop requirements for sensitive tools like `delete_table` to prevent accidental data loss.

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