How to Use the Typesense Vector Search MCP in OpenAI Agents SDK

Q: Can I manage multiple indexes using Typesense Vector Search with OpenAI Agents SDK?

Yep. The listvectorcollections tool lets your agent see every available index in the instance. You then target any of those collections when you use tools like indexdocument or searchvectors.

Run semantic searches and manage indices with the OpenAI Agents SDK.

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Connect Typesense Vector Search MCP to OpenAI Agents SDK

Create your Vinkius account to connect Typesense Vector Search to OpenAI Agents SDK 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 Typesense Vector Search with OpenAI Agents SDK

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Manage search data via MCP Server

You can programmatically build out your vector database. Use `create_collection` to define a new schema, or run `list_vector_collections` to see what's already there. Need to update the data? Just call `index_document`. This tool adds or updates documents in the specified collection without you having to write boilerplate code.

Perform vector search with OpenAI Agents SDK

Want to find similar content across huge datasets? The `search_vectors` tool handles that. You provide a collection name, a text query, and the vector string—it runs the whole thing. It combines semantic search (the vectors) with standard keyword filtering, giving you highly specific results right from your agent's workflow.

Monitor and clean up data in MCP Server

Sometimes you need to prune old or bad records. The `delete_document` tool permanently removes a document by its ID—be careful with this one, it’s irreversible. If you ever need to check the schema of a collection before using it, run `get_collection_details`. It spits out all the metadata you need.

Setup guide

Set up Typesense Vector Search MCP in OpenAI Agents SDK

Prerequisites

Python 3.10+ installed
openai-agents package (pip install openai-agents)
Active Vinkius subscription with a valid endpoint token

1

Install the SDK

Run pip install openai-agents to install the OpenAI Agents SDK. The MCP integration is built-in — no extra dependencies needed.
2

Connect via SSE transport

Use MCPServerSse with your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. The SDK auto-discovers all Typesense Vector Search tools at runtime.
3

Create your Agent

Pass the MCP to Agent(mcp_servers=[server]). The agent receives Typesense Vector Search tools as native definitions — JSON schemas resolve automatically.
4

Run the agent

Call Runner.run(agent, prompt) to execute. The agent invokes the appropriate Typesense Vector Search tools and returns structured results. Copy the full example on the right to get started.

agent.py

import asyncio
from agents import Agent, Runner
from agents.mcp import MCPServerSse

async def main():
    async with MCPServerSse(
        url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    ) as server:
        agent = Agent(
            name="Typesense Vector Search Agent",
            instructions="You have access to Typesense Vector Search tools.",
            mcp_servers=[server],
        )
        result = await Runner.run(agent, "List recent transactions")
        print(result.final_output)

asyncio.run(main())

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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 OpenAI Agents SDK

The agent uses the MCP Server to execute search operations directly. You pass a text query and a vector string to `search_vectors`, and your AI client gets back the results immediately.

This MCP Server primarily handles structured data, specifically documents containing text fields, unique IDs, and high-dimensional vectors. It’s all managed under collection schemas.

Yep. The `list_vector_collections` tool lets your agent see every available index in the instance. You then target any of those collections when you use tools like `index_document` or `search_vectors`.

No, you can combine text filtering with the vector search. The `search_vectors` tool accepts both a descriptive text query and the required vector format, giving you powerful results.

The server touches document content (text and vectors). Since your agent is running with built-in guardrails, it validates actions before they hit the MCP Server endpoint.

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