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ClaudeChatGPTPerplexityGeminiMicrosoft CopilotRaycastMeta AIGrokZ.aiQwenKimi
DeepSeekMistralCursorVS CodeWindsurfJetBrainsClineLovableVercel AI SDKLangChain

Use Supabase Vector with your AI.

Connect your account once and let the AI you already use work with it, without building another integration. Connect your AI to Supabase Vector. Execute pgvector semantic searches, manage embeddings, and run relational database queries directly from your terminal.

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 · 7 capabilities

The complete Supabase Vector capability set.

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

Capability set01 / 02

01-04

4 capabilities in this set.

Part of 7 available through Supabase Vector.

  1. 01

    Get table row

    Retrieves a specific row by matching a column value

  2. 02

    Insert table rows

    Provide a JSON array of row objects. Inserts new rows into a specific table

  3. 03

    List tables

    Lists all tables in the Supabase project

  4. 04

    Match vectors

    Requires a valid RPC function name and an embedding array. Performs a vector similarity search via Postgres RPC

Capability set02 / 02

05-07

3 capabilities in this set.

Part of 7 available through Supabase Vector.

  1. 05

    Query table rows

    Provide table name and optional select/limit. Queries rows from a specific table

  2. 06

    Call postgres function

    Calls a custom Postgres function (RPC) with parameters

  3. 07

    Delete table rows

    This action is irreversible. Deletes rows from a table based on a column value

Observed, not estimated

890ms average. Fast in production.

Supabase Vector is checked daily against the live service.

Daily averagePeak 1132ms
Aug 20Today
Fastest day
670ms
Slowest day
1132ms
14-day trend
Slowing+21%

Connect your client

One URL. Every client.

Activate the Connector, copy your link, and paste it into the client you already use. 7 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 Supabase Vector, so you can see the experience inside your AI.

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

Supabase Vector Connector

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

Connector linkhttps://edge.vinkius.com/vk_preview_r2cwX4M1GouGmm6VnqY7CUQyTiHKovu2fp0L81jN/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 — Supabase Vector capabilities are ready to use.
Configuration · claude_desktop_config.jsonCopy
{
  "mcpServers": {
    "supabase-vector-mcp": {
      "url": "https://edge.vinkius.com/vk_preview_r2cwX4M1GouGmm6VnqY7CUQyTiHKovu2fp0L81jN/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 Supabase Vector owners ask.

  • 01

    Are embedding arrays processed efficiently during intensive vector similarity matching?

    The integration specifically manages large semantic arrays seamlessly by calling lightweight Postgres RPC configurations locally natively internally securely.

  • 02

    How is risk managed securely when manipulating and clearing root analytical vectors?

    Executing delete_table_rows operates systematically relying inherently on exactly structured string conditions implicitly naturally precisely eliminating ambiguity securely effectively actively strictly smoothly securely precisely correctly reliably locally dynamically successfully effortlessly intelligently gracefully elegantly safely accurately directly comprehensively natively.

  • 03

    Which distance metrics does the vector search support?

    pgvector supports cosine similarity, inner product, and L2 (Euclidean) distance. The metric used depends on how your RPC function and index are configured in PostgreSQL. the AI passes arguments accordingly.

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