How to Use the Typesense Vector Search MCP in Mastra AI

Q: How do I trigger a search using Typesense Vector Search with Mastra AI?

You incorporate the searchvectors tool into your workflow. By supplying the collection, query text, and vector string, the agent executes the lookup as part of its overall process.

Q: Can I check my schema using Typesense Vector Search with Mastra AI?

Absolutely. The getcollectiondetails tool retrieves all necessary metadata for a specific collection, letting you verify the structure before writing any data.

Q: What if I need to delete old records using Typesense Vector Search with Mastra AI?

You use deletedocument. Be warned: this is an irreversible action. Always confirm the document ID before letting your workflow execute that step.

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Connect Typesense Vector Search MCP to Mastra AI

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Setup Typesense Vector Search with Mastra AI

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Perform Vector Search in the Mastra AI Workflow

When a workflow needs contextual data, it calls `search_vectors`. You feed it the collection name, text query, and vector string. The system returns relevant document matches for your multi-step process. This allows complex workflows to make decisions based on real-time semantic knowledge from the Typesense Vector Search MCP Server.

Define Data Structures with Mastra AI

Starting a new data set? Use `create_collection` to define your schema by passing a JSON object. This step is crucial because it dictates what kind of data the collection accepts. After defining the structure, you can use `list_vector_collections` to confirm that the resource exists and is ready for indexing.

Maintain Data State via MCP Server

If a document needs an update or initial entry, call `index_document`. You specify the collection name and provide the document data as JSON. The server handles adding or updating that specific record. Don't forget to use `get_collection_details` if you need to verify the schema before attempting any writes.

Setup guide

Set up Typesense Vector Search 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 Typesense Vector Search 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: "typesense-vector-search-mcp-client",
  servers: {
    "typesense-vector-search-mcp": {
      url: new URL(
        "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
      ),
    },
  },
});

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

const result = await agent.generate(
  "List recent Typesense Vector Search transactions"
);
console.log(result.text);

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Typesense Vector Search. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

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Common questions about Typesense Vector Search MCP in Mastra AI

You incorporate the `search_vectors` tool into your workflow. By supplying the collection, query text, and vector string, the agent executes the lookup as part of its overall process.

Absolutely. The `get_collection_details` tool retrieves all necessary metadata for a specific collection, letting you verify the structure before writing any data.

You use `delete_document`. Be warned: this is an irreversible action. Always confirm the document ID before letting your workflow execute that step.

This server touches search collection schemas and document content. All types of vector data are managed by Typesense Vector Search, keeping your information segmented.

You can create, read, update, delete, list collections, and run semantic searches. The full set of tools is available for complex workflow management.

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