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Connect Typesense Vector Search to ChatGPT and Claude

Set it up once and it works in ChatGPT, Claude, Cursor, and any other client you love. It all runs safely in the background, so you can relax and focus on what really matters. Manage vector collections and semantic queries through natural conversation.

Ask AI about this Connector

Developed, maintained, and hosted by Vinkius.

MCP VERIFIED · PRODUCTION READY · VINKIUS GUARANTEED

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Works with modern AI clients that support MCP, including ChatGPT, Claude, Cursor, and more.

ChatGPTClaudeCursorPerplexityGeminiMicrosoft CopilotRaycastMeta AI

Your AI does the work. We keep it safe and sound.

No other AI tool gives you this. Your workflow runs in the client you already love, your account stays protected, and you decide what the agent can and cannot do. It is the whole reason to make the switch.

Each Connector lives inside our infrastructure, the same place where your workflow runs. The three things below are already part of it, working quietly in the background so you never have to think about them. Nothing to add, nothing to set up, nothing to worry about.

A futuristic MCP client docking bay
V8 Isolate01

Each Connector runs inside its own sealed environment.

Each Connector runs inside its own sealed V8 runtime — no access to your files, your system, or other servers. A Connector never sees past its own request.

Kill Switch02

One switch stops every agent. Instantly.

One action deactivates every Connector, revokes every token, and ends every session at once. AI Governance keeps the record.

FinOps per User03

Spending limits that pause instead of surprise.

Every user keeps their own connections and credentials, isolated from everyone else. Anything that would cross your spending limit is held for your approval, not billed.

Three controls, built into the layer your AI connects through.

Curated set · 6 capabilities

We ship only the actions that matter.

Capability set01 / 02

01-03

3 capabilities in this set.

Part of 6 available through Typesense Vector Search.

  1. 01

    Search vectors

    This capability runs vector similarity searches combined with text filtering. You provide the collection name, a text query, and a vector query string to find relevant matches.

  2. 02

    List vector collections

    This capability retrieves a list of all existing collections within your Typesense instance.

  3. 03

    Create collection

    This capability provisions a new search collection. You pass a JSON object containing the specific schema details to set it up.

Capability set02 / 02

04-06

3 capabilities in this set.

Part of 6 available through Typesense Vector Search.

  1. 04

    Delete document

    This capability permanently removes a specific document from a collection using its ID. This action cannot be undone.

  2. 05

    Index document

    This capability adds or updates a document in a search collection. You simply provide the collection name and the JSON document data.

  3. 06

    Get collection details

    This capability fetches the schema and metadata for a specific collection to help you verify field mappings.

Real work

What teams actually run

01

RAG Data Ingestion

Hand a JSON object to your agent to quickly add new knowledge base documents to your vector index.

02

Rapid Prototyping

Ask your agent to spin up a new collection with a specific embedding dimension for a new project.

03

Database Auditing

Use the agent to check collection schemas and verify that field types match your application requirements.

04

Search Testing

Run vector similarity searches with specific text filters to test how relevant your results are.

Automate a task

Put Typesense Vector Search to work on one thing.

Step-by-step automations your AI runs through this Connector: the workflow, the prompts to start with, and the exact capabilities behind each move.

One connector, every AI

Typesense Vector Search works with the most popular AI clients.

These are the most popular clients, each with a step-by-step guide: one link, set up once, with governance and visibility built in. And because everything runs on the MCP standard, the same connection also works in any other compatible client — nothing to rebuild.

ClaudeClaudeChatGPTChatGPTGeminiGeminiPerplexityPerplexityGrokGrokMicrosoft CopilotMicrosoft CopilotCursorCursorVS CodeVS CodeWindsurfWindsurfJetBrainsJetBrainsClineClineLangChainLangChainVercel AI SDKVercel AI SDKLovableLovableZ.aiZ.aiRaycastRaycastQwen CodeQwen CodeKimi CodeKimi CodeLe ChatLe Chat

Building your own app? The connector is yours to use.

You don't need a client to put Typesense Vector Search to work: the same hosted connection plugs into your own applications and agent code, with the same governance on every request. Build with it, chat with it — one connection for both.

Observed, not estimated

938ms average. Fast in production.

Typesense Vector Search is checked daily against the live service.

Daily averagePeak 1180ms
Oct 3Today
Fastest day
691ms
Slowest day
1180ms
14-day trend
Slowing+14%

Connect your client

One URL. Every client.

Activate the Connector, copy your link, and paste it into the client you already use. 6 capabilities arrive ready to run.

Preview access · not provider authentication

The vk_preview_* token belongs to Vinkius preview infrastructure. It lets Claude discover and display the capabilities of Typesense Vector Search, so you can see the experience inside your AI.

It does not authenticate your account with Typesense Vector Search. Actions requiring credentials or live account data may not run until you activate the Connector and authorize the service.

Typesense Vector Search Connector

You're all set. Choose your MCP client and follow the setup instructions.

Connector linkhttps://edge.vinkius.com/vk_preview_bmlNohSGzzJmxN6goNUndx2uYrEvS88xXUYpe92K/mcp

Claude Desktop

Follow the steps below to connect in seconds.

  1. 1In Claude Desktop, open Settings → Connectors.
  2. 2Click “Add custom connector” and paste the connector link above as the remote MCP server URL.
  3. 3Click Add and start a new chat — Typesense Vector Search capabilities are ready to use.
Configuration · claude_desktop_config.jsonCopy
{
  "mcpServers": {
    "typesense-vector-search-mcp": {
      "url": "https://edge.vinkius.com/vk_preview_bmlNohSGzzJmxN6goNUndx2uYrEvS88xXUYpe92K/mcp"
    }
  }
}
  • Claude
  • ChatGPT
  • Cursor
  • VS Code
  • Windsurf
  • Claude Code
  • JetBrains
  • Cline

Step-by-step instructions for each client are in the guide. How to connect

Guided setup for Claude? Set up Typesense Vector Search in Claude and other AIs

See all the AI clients this connector works with ↑

Who it's for

Built for the work Typesense Vector Search owners hand off.

This MCP is built for technical users who need to interact with vector databases without writing repetitive boilerplate code.

  • 01

    AI Application Builders

    They prompt the agent to create semantic collections with specific float array logic.

  • 02

    Data Engineers

    They use the agent to manually ingest missing RAG reference documents into active collections.

  • 03

    Backend Developers

    They perform sanity checks and inspect relevance scores using text-filtered semantic searches.

FAQ

Questions Typesense Vector Search owners ask.

  • 01

    How do I connect this MCP to my Typesense instance?

    You just need to provide your active Typesense Host URL and an Admin API Key during the setup process in Vinkius.

  • 02

    Which AI clients can use this MCP?

    This MCP works with any compatible client, including Claude, Cursor, and Windsurf.

  • 03

    Can I create new collections using my AI agent?

    Yes, you can instruct your agent to create new collections by providing the required JSON schema.

  • 04

    Is document deletion permanent?

    Yes, using the delete_document capability removes the document from the collection immediately and irreversibly.

  • 05

    Does this support semantic search?

    Yes, the search_vectors capability allows for vector similarity searches combined with text filtering.

  • 06

    Can the agent perform vector plus text-filtering search combined natively?

    Yes. Provide the agent with the collection name alongside the text payload and tell it the exact vector structure. It leverages internal filters querying natively and returns the nearest neighbors with exact accuracy scores.

  • 07

    How do I make the AI create a semantic collection ready for embeddings (OpenAI 1536 dims)?

    Ask the agent to use 'create_collection'. Provide standard JSON declaring the name, the field structure, and explicitly define the float[] field tracking the 1536 dims length. The cluster will spin the framework up instantly.

  • 08

    Can it delete problematic vectors holding bad geometry data manually?

    Absolutely. Supplying the explicit collection target and the item 'id' to the delete_document prompt securely wipes out all traces from the dataset. Use this sparingly as it can't be undone easily.

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