What is pgvector MCP?

It is an MCP that connects your AI client to a PostgreSQL database equipped with the pgvector extension, allowing for vector similarity searches and table management.

Which AI clients can use this MCP?

You can use this with any MCP-compatible client, including Claude, Cursor, Windsurf, and VS Code.

Do I need to write SQL to use this?

No. You can perform searches, create tables, and manage indexes using natural language prompts through your AI agent.

Can I optimize my search speed?

Yes. You can instruct your agent to create HNSW or IVFFlat indexes on your vector columns to accelerate queries.

Does this require a separate vector database?

No. This MCP works directly with your existing PostgreSQL database using the pgvector extension.

Does the agent connect directly to my database?

Yes. Your connection string is encrypted at rest and injected into an isolated runtime. The agent connects directly to your PostgreSQL instance. no intermediate proxies, no data copies, no third-party storage.

What vector dimensions are supported?

Any dimension supported by pgvector. from small 128-d vectors to large 3072-d embeddings (e.g., OpenAI text-embedding-3-large). Specify the dimension when creating a table and the agent handles the rest.

Which distance metrics can I use for similarity search?

pgvector supports three operators: ` (L2/Euclidean distance), (cosine distance), and ` (negative inner product). The agent uses cosine distance by default, which works best for normalized embeddings like those from OpenAI.

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Connect Pgvector Vector Database 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. Turn your existing database into a high-performance vector store.

Ask AI about this Connector

Developed, maintained, and hosted by Vinkius.

MCP VERIFIED · PRODUCTION READY · VINKIUS GUARANTEED

Waiting for input…

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

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.

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 pgvector.

01
Create index
This capability builds vector indexes like HNSW or IVFFlat to speed up your similarity searches.
02
Create table
Use this to set up new tables specifically designed to hold vector embeddings.
03
List tables
This capability lets your agent see which tables in your database contain vector columns.

Capability set02 / 02

04-06

3 capabilities in this set.

Part of 6 available through pgvector.

04
Search vectors
This capability performs nearest-neighbor searches using cosine, L2, or inner product metrics.
05
Insert vector
Use this to add new vector entries and associated metadata to your tables.
06
Delete vector
This capability removes specific vector entries from your database.

Real work

What teams actually run

RAG Pipeline Development

Query production embeddings directly to provide context to your agent.

Semantic Search Implementation

Add vector-based search to existing applications using your current database.

Database Optimization

Create and manage HNSW indexes to reduce query latency on large datasets.

Knowledge Base Management

Keep your embeddings fresh by inserting and deleting vectors through natural language.

Automate a task

Put pgvector 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.

How to optimize vector search

One connector, every AI

pgvector 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.

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

You don't need a client to put pgvector 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

897ms average. Fast in production.

pgvector is checked daily against the live service.

Daily averagePeak 1031ms

Oct 3Today

Fastest day: 763ms
Slowest day: 1031ms
14-day trend: Stable0%

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 pgvector, so you can see the experience inside your AI.

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

pgvector Connector

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

Connector linkhttps://edge.vinkius.com/vk_preview_m3Dmr7TKd7NjkaPwx4OA0eTWiY7yo9GZdmp9NSJ4/mcp

Claude Desktop

Follow the steps below to connect in seconds.

1In Claude Desktop, open Settings → Connectors.
2Click “Add custom connector” and paste the connector link above as the remote MCP server URL.
3Click Add and start a new chat — pgvector capabilities are ready to use.

Configuration · claude_desktop_config.jsonCopy

{
  "mcpServers": {
    "pgvector-vector-database-mcp": {
      "url": "https://edge.vinkius.com/vk_preview_m3Dmr7TKd7NjkaPwx4OA0eTWiY7yo9GZdmp9NSJ4/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 pgvector in Claude and other AIs

See all the AI clients this connector works with ↑

Who it's for

Built for the work pgvector owners hand off.

This MCP is built for technical users who want to integrate vector capabilities into existing PostgreSQL workflows.

01
AI Engineers
They use it to build RAG pipelines without writing custom API endpoints.
02
Data Architects
They use it to manage vector schemas and monitor index performance.
03
Fullstack Developers
They use it to add semantic search to apps using their existing database.

FAQ

Questions pgvector owners ask.

01
What is pgvector MCP?
It is an MCP that connects your AI client to a PostgreSQL database equipped with the pgvector extension, allowing for vector similarity searches and table management.
02
Which AI clients can use this MCP?
You can use this with any MCP-compatible client, including Claude, Cursor, Windsurf, and VS Code.
03
Do I need to write SQL to use this?
No. You can perform searches, create tables, and manage indexes using natural language prompts through your AI agent.
04
Can I optimize my search speed?
Yes. You can instruct your agent to create HNSW or IVFFlat indexes on your vector columns to accelerate queries.
05
Does this require a separate vector database?
No. This MCP works directly with your existing PostgreSQL database using the pgvector extension.
06
Does the agent connect directly to my database?
Yes. Your connection string is encrypted at rest and injected into an isolated runtime. The agent connects directly to your PostgreSQL instance. no intermediate proxies, no data copies, no third-party storage.
07
What vector dimensions are supported?
Any dimension supported by pgvector. from small 128-d vectors to large 3072-d embeddings (e.g., OpenAI text-embedding-3-large). Specify the dimension when creating a table and the agent handles the rest.
08
Which distance metrics can I use for similarity search?
pgvector supports three operators: ` (L2/Euclidean distance), (cosine distance), and ` (negative inner product). The agent uses cosine distance by default, which works best for normalized embeddings like those from OpenAI.

Explore

More in Loved By Devs

Connect pgvector to Claude, Cursor, ChatGPT & more

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

Each Connector runs inside its own sealed environment.

One switch stops every agent. Instantly.

Spending limits that pause instead of surprise.

We ship only the actions that matter.

01-03

Create index

Create table

List tables

04-06

Search vectors

Insert vector

Delete vector

What teams actually run

RAG Pipeline Development

Semantic Search Implementation

Database Optimization

Knowledge Base Management

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

pgvector Connector

Claude Desktop

AI Engineers

Data Architects

Fullstack Developers

What is pgvector MCP?

Which AI clients can use this MCP?

Do I need to write SQL to use this?

Can I optimize my search speed?

Does this require a separate vector database?

Does the agent connect directly to my database?

What vector dimensions are supported?

Which distance metrics can I use for similarity search?

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