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ClaudeChatGPTPerplexityGeminiMicrosoft CopilotRaycastMeta AIGrokZ.aiQwenKimi
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Use Typesense Vector Search with your AI.

Connect your account once and let the AI you already use work with it, without building another integration. Automate vector similarity searches via Typesense. index documents, manage collections, and execute semantic queries directly from your AI agent.

Included with plan

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

Complete set · 6 capabilities

The complete Typesense Vector Search capability set.

These are the exact actions your AI can choose when you ask it to work with Typesense Vector Search.

Capability set01 / 02

01-03

3 capabilities in this set.

Part of 6 available through Typesense Vector Search.

  1. 01

    Search vectors

    Provide the collection name, a text query, and a vector_query string (e.g., "vec:(0.1, 0.2, ...)"). Performs a vector similarity search combined with optional text filtering

  2. 02

    List vector collections

    Lists all collections in the Typesense instance

  3. 03

    Create collection

    Provide the schema details as a JSON object. Creates a new search collection with a specific schema

Capability set02 / 02

04-06

3 capabilities in this set.

Part of 6 available through Typesense Vector Search.

  1. 04

    Delete document

    This action is irreversible. Permanently removes a document from a collection by its ID

  2. 05

    Index document

    Provide the collection name and the document data as a JSON object. Adds or updates a document in a search collection

  3. 06

    Get collection details

    Retrieves schema and metadata for a specific collection

Observed, not estimated

868ms average. Fast in production.

Typesense Vector Search is checked daily against the live service.

Daily averagePeak 1083ms
Aug 20Today
Fastest day
680ms
Slowest day
1083ms
14-day trend
Slowing+18%

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

FAQ

Questions Typesense Vector Search owners ask.

  • 01

    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.

  • 02

    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.

  • 03

    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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