How to Use the pgvector (Vector Database) MCP in Claude

Q: Can I create new embedding schemas with pgvector (Vector Database) inside Claude Desktop?

Yes. Your agent uses the createtable tool to set up the vector columns with your specified dimensions, followed by createindex to optimize performance.

Q: What happens when I use pgvector (Vector Database) to delete records in Claude Desktop?

The agent calls deletevector with the target row identifier. The vector is instantly purged from your PostgreSQL tables, and Claude Desktop confirms the deletion.

Q: Do I need to install a separate database client to see my vector tables?

No. The listtables tool lets your agent inspect your database schema and display all vector-enabled tables directly in your conversation.

Run raw vector similarity queries directly from your Claude Desktop chat without leaving your local environment.

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Connect pgvector (Vector Database) MCP to Claude Desktop

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Setup pgvector (Vector Database) with Claude Desktop

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Fast vector table setup in Claude Desktop

The `create_table` tool sets up pgvector columns with defined dimensions directly in your local Postgres database for Claude Desktop. Your local Claude Desktop agent calls this tool to establish the exact dimensions you need for your Postgres embeddings. No manual SQL migrations or terminal switching are required inside your Claude Desktop workspace. After the database schema is ready, you configure your pgvector indexing strategy on the fly through the chat interface. The tool `create_index` handles the heavy lifting, setting up HNSW or IVFFlat indexes so your Claude Desktop similarity lookups don't crawl to a halt as your Postgres database grows.

Direct embedding ingestion from your chat

The `insert_vector` tool lets your Claude Desktop client write high-dimensional embeddings straight into your Postgres database tables using this MCP connection. You feed raw text or array embeddings directly to the Claude Desktop chat interface, and the agent maps the inputs to your pgvector columns. It handles the formatting hurdles so you don't write boilerplate Postgres insert scripts inside Claude Desktop. If a vector becomes stale or you need to prune your dataset, the `delete_vector` tool removes specific records from Postgres instantly. This keeps your local development database clean and ensures your Claude Desktop retrieval context remains highly accurate.

Immediate semantic search within your workspace

The `search_vectors` tool executes similarity queries inside your PostgreSQL database to find the closest matching documents via this Claude Desktop MCP Server. It uses native pgvector operators to calculate distances, pulling the most relevant rows back into your active Claude Desktop context window. To keep track of your schema, the `list_tables` tool gives Claude Desktop a clear view of your existing Postgres vector relations. You always know exactly which pgvector tables are available for querying without running external database clients from Claude Desktop.

Setup guide

Set up pgvector (Vector Database) MCP in Claude Web or Desktop

1

Open Claude Settings

Go to claude.ai, click your profile icon, then navigate to Customize → Connectors.
2

Add Custom Connector

Click the "+" button and select Add custom connector. Paste your Vinkius endpoint URL: https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. For OAuth-protected servers, expand Advanced settings to add credentials.
3

Start a conversation

Open a new chat. The pgvector (Vector Database) MCP tools are available immediately — no restart needed.

Endpoint URL

https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp

No configuration file needed — paste the URL directly in the Claude web interface.

Available on Free (1 connector), Pro, Max, Team, and Enterprise plans.

Prerequisites

Claude Desktop installed (macOS or Windows)
Active Vinkius subscription with a valid endpoint token

1

Open Claude Desktop Settings

Click the menu icon at the top-left corner, go to Settings → Developer → Edit Config. This opens claude_desktop_config.json in your default text editor.
2

Paste the pgvector (Vector Database) MCP configuration

Copy the JSON snippet on the right into the mcpServers object. Replace [YOUR_TOKEN_HERE] with your endpoint token from cloud.vinkius.com.
3

Restart Claude Desktop

Close and reopen the application. Claude needs a full restart to load new MCPs — refreshing a conversation is not enough.
4

Verify the connection

Open a new conversation. Click the 🔌 icon at the bottom of the message input. You should see tools listed under pgvector-vector-database-mcp.

json

{
  "mcpServers": {
    "pgvector-vector-database-mcp": {
      "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    }
  }
}

Get your connection token →

Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by pgvector. 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 pgvector (Vector Database) MCP in Claude Desktop

You ask your chat agent to find similar items. The agent invokes the `search_vectors` tool, queries your database, and returns the closest records directly in your chat window.

Yes. Your agent uses the `create_table` tool to set up the vector columns with your specified dimensions, followed by `create_index` to optimize performance.

The agent calls `delete_vector` with the target row identifier. The vector is instantly purged from your PostgreSQL tables, and Claude Desktop confirms the deletion.

No. The `list_tables` tool lets your agent inspect your database schema and display all vector-enabled tables directly in your conversation.

Yes. This MCP Server runs locally on your machine via Claude Desktop, meaning your database credentials and vector embeddings never leave your local environment or traverse third-party servers.

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