Bring Embeddings
to Cursor
Create your Vinkius account to connect pgvector (Vector Database) to Cursor and start using all 6 AI tools in minutes. Fully managed, enterprise secure, and ready to use without writing a single line of code. No hosting, no server setup — just connect and start using.
GDPR Free for SubscribersCompatible with every major AI agent and IDE
Gemini
What is the pgvector (Vector Database) MCP Server?
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
- Subscribe to the pgvector integration on the marketplace.
- Paste your PostgreSQL connection string (e.g.,
postgresql://user:pass@host:5432/db). - 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.
Built-in capabilities (6)
Create vector index
Create vector table
Delete a vector
Insert a vector
List tables
Vector similarity search
Why Cursor?
Cursor's Agent mode turns pgvector (Vector Database) into an in-editor superpower. Ask Cursor to generate code using live data from pgvector (Vector Database) and it fetches, processes, and writes. all in a single agentic loop. 6 tools appear alongside file editing and terminal access, creating a unified development environment grounded in real-time information.
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Agent mode turns Cursor into an autonomous coding assistant that can read files, run commands, and call MCP tools without switching context
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Cursor's Composer feature can generate entire files using real-time data fetched through MCP. no copy-pasting from external dashboards
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MCP tools appear alongside built-in tools like file reading and terminal access, creating a unified agentic environment
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VS Code extension compatibility means your existing workflow, keybindings, and extensions all work alongside MCP tools
pgvector (Vector Database) in Cursor
Why run pgvector (Vector Database) with Vinkius?
The pgvector (Vector Database) connection runs on our fully managed, secure cloud infrastructure. We handle the hosting, maintenance, and security so you don't have to deal with servers or code. All 6 tools are ready to work instantly without any complex setup.
You stay in complete control of your data. Your AI only accesses the information you approve, keeping your sensitive passwords and private details completely safe. Plus, with automatic optimizations, your AI works faster and more efficiently.
* Every connection is hosted and maintained by Vinkius. We handle the security, updates, and infrastructure so you don't have to write code or manage servers. See our infrastructure
Over 4,000 integrations ready for AI agents
Explore a vast library of pre-built integrations, optimized and ready to deploy.
Connect securely in under 30 seconds
Generate tokens to authenticate and link external services in a single step.
Complete visibility into every agent action
Audit live requests, latency, success rates, and active security compliance policies.
Optimize spending and track token ROI
Analyze real-time token consumption and cost metrics detailed by connection.
Explore our live AI Agents Analytics dashboard to see it all working
This dashboard is included when you connect pgvector (Vector Database) using Vinkius. You will never be left in the dark about what your AI agents are doing with your tools.
pgvector (Vector Database) and 4,000+ other AI tools. No hosting, no code, ready to use.
Professionals who connect pgvector (Vector Database) to Cursor through Vinkius don't need to write code, manage servers, or worry about security. Everything is pre-configured, secure, and runs automatically in the background.
Raw MCP | Vinkius | |
|---|---|---|
| Ready-to-use MCPs | Find and configure each manually | 4,000+ MCPs ready to use |
| Connection Setup | Manual coding & server setup | 1-click instant connection |
| Server Hosting | You host it yourself (needs 24/7 uptime) | 100% hosted & managed by Vinkius |
| Security & Privacy | Stored in plaintext config files | Bank-grade encrypted vault |
| Activity Visibility | Blind execution (no logs or tracking) | Live dashboard with real-time logs |
| Cost Control | Runaway AI token spend risk | Automatic budget limits |
| Revoking Access | Must delete files or code to stop | 1-click disconnect button |
How Vinkius secures
pgvector (Vector Database) for Cursor
Every request between Cursor and pgvector (Vector Database) is protected by our secure gateway. We automatically keep your sensitive data private, prevent unauthorized access, and let you disconnect instantly at any time.
Frequently asked questions
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.
What is Agent mode and why does it matter for MCP?
Agent mode is Cursor's autonomous execution mode where the AI can perform multi-step tasks: reading files, editing code, running terminal commands, and calling MCP tools. Without Agent mode, Cursor operates in a simpler ask-and-answer mode that doesn't support tool calling. Always ensure you're in Agent mode when working with MCP servers.
Where does Cursor store MCP configuration?
Cursor looks for MCP server configurations in a mcp.json file. You can configure servers at the project level (.cursor/mcp.json in your project root) or globally (~/.cursor/mcp.json). Project-level configs take precedence.
Can Cursor use MCP tools in inline edits?
No. MCP tools are only available in Agent mode through the chat panel. Inline completions and Tab suggestions do not trigger MCP tool calls. This is by design. tool calls require user visibility and approval.
How do I verify MCP tools are loaded?
Open Settings → Features → MCP and look for your server name. A green indicator means the server is connected. You can also check Agent mode's available tools by clicking the tools dropdown in the chat panel.
Tools not appearing in Cursor
Ensure you are in Agent mode (not Ask mode). MCP tools only work in Agent mode.
Server shows as disconnected
Check Settings → Features → MCP and verify the server status. Try clicking the refresh button.
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