pgvector (Vector Database) MCP, Ready to Go

Use pgvector MCP with Claude or Cursor to run semantic search and manage embeddings directly inside your PostgreSQL database.

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Run semantic search and manage embeddings inside your PostgreSQL database.

pgvector MCP for AI Agents

Works with every AI agent you already use

…and any MCP-compatible client

How fast is the pgvector (Vector Database) MCP Server?

1001ms Fast

Fast Acceptable Slow

Average time for the server to become ready for requests over the last 13 days, measured until the initialize / tools/list handshake completes. Metrics are updated daily between 00:00 and 04:00 UTC. Create a free account, use this MCP on Vinkius Cloud, and connect it to your AI agent in seconds.

Min 833ms

Average 1001ms

Max 2755ms

Trend (improving) ↓ 33%

Daily latency

2755ms 7/6/2026

2251ms 7/7/2026

1076ms 7/8/2026

1059ms 7/9/2026

924ms 7/10/2026

1036ms 7/11/2026

1406ms 7/12/2026

994ms 7/13/2026

959ms 7/14/2026

960ms 7/15/2026

994ms 7/16/2026

833ms 7/17/2026

945ms 7/18/2026

7/6/2026 7/18/2026

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AI Agent

What AI agents can do with pgvector 6-tool PostgreSQL Vector Search

Perform similarity searches, manage tables, and optimize indexes in PostgreSQL.

Search vectors

Run a similarity search to find the closest matches for a specific query. This helps you find relevant data based on meaning rather than just keywords.

List tables

View a list of all tables in your database. Use this to quickly identify which ones contain your vector data.

Insert vector

Add a new vector and its associated metadata into an existing table. This keeps your knowledge base updated with new entries.

Create table

Build a new table with the specific dimensions required for your embeddings. This sets up the correct structure for your vector data.

Create index

Set up HNSW or IVFFlat indexes on your vector columns. This makes your similarity searches much faster as your dataset grows.

Delete vector

Remove a specific vector entry from your database. This is useful for keeping your data clean and up to date.

One MCP enables access. Vinkius turns MCPs into production-ready infrastructure.

You're looking at one of 5,700+ managed MCPs. The real value isn't the catalog. It's the control plane that secures, governs, audits, and manages every interaction between your agents and the tools they use.

No Shadow AI

Every agent action is visible, approved, and auditable. Nothing runs outside your governance.

Absolute agent control

Fine-grained permissions for every agent, MCP, and tool. Instantly revoke access and audit every execution.

Cost control per token

Spend broken down to the token, tool, and agent. Budgets and hard limits. No surprise invoices.

Managed & monitored infra

We operate the runtime, authentication, scaling, retries, and monitoring. Your team manages AI, not infrastructure.

Data protection, DLP by design

Sensitive data is filtered before reaching the model. Access is governed so agents receive only the information they're allowed to use.

Token optimization, real savings

Lower AI costs by delivering the right context instead of unnecessary tools. Better accuracy, faster responses, and fewer wasted tokens.

pgvector PostgreSQL Vector Search for RAG Pipelines

This is for the developer who wants to keep their architecture simple and the data architect who doesn't want to manage a separate vector database.

AI Engineer

Building RAG pipelines that need to query production embeddings without extra API hops.

Data Architect

Managing vector schemas and monitoring index performance from a single chat interface.

Fullstack Developer

Adding semantic search to an existing app without spinning up new infrastructure.

Frequently Asked Questions

Can pgvector MCP help me build a RAG system? +

Yes, it allows your agent to query embeddings directly from your PostgreSQL database. This makes it easy to build a retrieval-augmented generation pipeline without needing a separate vector database.

How do I use pgvector MCP to speed up my searches? +

You can ask your agent to create HNSW or IVFFlat indexes on your vector columns. These indexes significantly reduce the time it takes to find the most similar results in large datasets.

Does pgvector MCP support different distance metrics? +

Yes, it supports common metrics like cosine, L2, and inner product. You can specify which one your agent should use when performing a similarity search.

Can I manage my embeddings directly with pgvector MCP? +

Absolutely. You can ask your agent to insert new embeddings, update existing ones, or delete old records directly within your PostgreSQL tables.

Is pgvector MCP better than a separate vector database? +

It depends on your needs. If you already use PostgreSQL and want to keep your data in one place, this is often the simplest choice. If you need specialized high-scale vector-only features, a dedicated store might be better.

How does pgvector MCP handle large datasets? +

It handles large scale data by using production-grade indexing. By creating HNSW or IVFFlat indexes, the MCP ensures your similarity searches remain fast even as your row count grows into the millions.

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.