pgvector (Vector Database) MCP Server
Run vector similarity searches, manage embedding tables, and build AI-powered retrieval pipelines — all directly inside your existing PostgreSQL database.
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What is the pgvector MCP Server?
The pgvector MCP Server gives AI agents like Claude, ChatGPT, and Cursor direct access to pgvector via 6 tools. Run vector similarity searches, manage embedding tables, and build AI-powered retrieval pipelines — all directly inside your existing PostgreSQL database. Powered by the Vinkius - no API keys, no infrastructure, connect in under 2 minutes.
Built-in capabilities (6)
Tools for your AI Agents to operate pgvector
Ask your AI agent "Show me all tables with vector columns in my database." and get the answer without opening a single dashboard. With 6 tools connected to real pgvector data, your agents reason over live information, cross-reference it with other MCP servers, and deliver insights you would spend hours assembling manually.
Works with Claude, ChatGPT, Cursor, and any MCP-compatible client. Powered by the Vinkius - your credentials never touch the AI model, every request is auditable. Connect in under two minutes.
Why teams choose Vinkius
One subscription gives you access to thousands of MCP servers - and you can deploy your own to the Vinkius Edge. Your AI agents only access the data you authorize, with DLP that blocks sensitive information from ever reaching the model, kill switch for instant shutdown, and up to 60% token savings. Enterprise-grade infrastructure and security, zero maintenance.
Build your own MCP Server with our secure development framework →Vinkius works with every AI agent you already use
…and any MCP-compatible client
pgvector (Vector Database) MCP Server capabilities
6 toolsCreate vector index
Create vector table
Delete a vector
Insert a vector
List tables
Vector similarity search
What the pgvector (Vector Database) MCP Server unlocks
Connect your PostgreSQL + pgvector database to any AI agent and manage vector embeddings, similarity searches, and index optimizations through natural conversation.
What you can do
- Vector Similarity Search — Run nearest-neighbor queries using cosine, L2, or inner product distance metrics across millions of embeddings with a single prompt.
- Table Management — Discover which tables contain vector columns, create new embedding tables with custom dimensions, and inspect your schema.
- Embedding CRUD — Insert, update, and delete individual vector entries with metadata, keeping your knowledge base fresh and accurate.
- Index Optimization — Create HNSW or IVFFlat indexes on vector columns to accelerate approximate nearest-neighbor (ANN) queries by orders of magnitude.
How it works
1. Subscribe to the pgvector integration on the marketplace.
2. Paste your PostgreSQL connection string (e.g., postgresql://user:pass@host:5432/db).
3. Ask your AI agent to search vectors, create tables, or optimize indexes.
Who is this for?
- AI Engineers — Build RAG (Retrieval-Augmented Generation) pipelines that query production embeddings without writing custom API endpoints.
- Data Architects — Manage vector schemas, monitor index performance, and optimize query latency from a single conversational interface.
- Fullstack Developers — Add semantic search to existing apps by querying pgvector directly through your AI agent, no new microservices needed.
Frequently asked questions about the pgvector (Vector Database) MCP Server
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) with your favorite client
Step-by-step setup guides for every MCP-compatible client and framework:
Anthropic's native desktop app for Claude with built-in MCP support.
AI-first code editor with integrated LLM-powered coding assistance.
GitHub Copilot in VS Code with Agent mode and MCP support.
Purpose-built IDE for agentic AI coding workflows.
Autonomous AI coding agent that runs inside VS Code.
Anthropic's agentic CLI for terminal-first development.
Python SDK for building production-grade OpenAI agent workflows.
Google's framework for building production AI agents.
Type-safe agent development for Python with first-class MCP support.
TypeScript toolkit for building AI-powered web applications.
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Give your AI agents the power of pgvector MCP Server
Production-grade pgvector (Vector Database) MCP Server. Verified, monitored, and maintained by Vinkius. Ready for your AI agents — connect and start using immediately.