Pinecone Cursor Plugin

May 13, 2026 · View on GitHub

Official Pinecone plugin for Cursor. Build semantic search, RAG, recommendation systems, and other vector-based applications with Pinecone — directly from your editor.

What's included

Skills

Skills are specialized agent capabilities invoked automatically by Cursor Agent or manually via /skill-name in chat.

Skill	What it does
`/pinecone-quickstart`	Step-by-step onboarding — create an index, upload data, and run your first search. Choose between a Database path (vector search) or Assistant path (document Q&A).
`/pinecone-query`	Search integrated indexes using natural language text via the Pinecone MCP server.
`/pinecone-cli`	Use the Pinecone CLI (`pc`) for terminal-based index and vector management.
`/pinecone-assistant`	Create, manage, and chat with Pinecone Assistants for document Q&A with citations. Includes scripts for uploading files, syncing changes, and retrieving context.
`/pinecone-full-text-search`	Create, ingest into, and query a Pinecone full-text-search (FTS) index using the preview API.
`/pinecone-mcp`	Reference documentation for all Pinecone MCP server tools and their parameters.
`/pinecone-docs`	Curated links to official Pinecone documentation, organized by topic.
`/pinecone-help`	Overview of all available skills and what you need to get started.

MCP Server

The plugin bundles the Pinecone MCP server (@pinecone-database/mcp), giving Cursor Agent direct access to your Pinecone resources:

Create, describe, and delete indexes
Upsert and query vectors
Search Pinecone documentation
Manage index configurations

Bundled Scripts

Several skills include Python scripts (run via uv) for operations beyond what MCP provides:

Script	Skill	Purpose
`upsert.py`	pinecone-quickstart	Seed an index with sample data
`quickstart_complete.py`	pinecone-quickstart	Standalone end-to-end quickstart
`create.py`	pinecone-assistant	Create a new Pinecone Assistant
`upload.py`	pinecone-assistant	Upload files to an assistant
`chat.py`	pinecone-assistant	Chat with an assistant
`context.py`	pinecone-assistant	Retrieve context snippets from an assistant
`list.py`	pinecone-assistant	List all assistants in your account
`sync.py`	pinecone-assistant	Sync local files to an assistant
`ingest.py`	pinecone-full-text-search	Bulk-ingest a prepared JSONL into an FTS index

Installation

Run the following command in Cursor chat:

/add-plugin pinecone

Or install directly from the marketplace: cursor.com/marketplace/pinecone

Prerequisites

Pinecone account — free at app.pinecone.io
API key — create one in the Pinecone console, then add it to a .env file at your workspace root:
```
PINECONE_API_KEY=your-key
```
The bundled MCP config loads this file via Cursor's envFile field, so you don't need to export the key in your shell. (If you prefer, export PINECONE_API_KEY="your-key" also works for terminal scripts.)
Node.js v18+ — required for the MCP server (npx)

Optional

Tool	What it enables	Install
Pinecone CLI (`pc`)	Terminal-based index management, batch operations	`brew tap pinecone-io/tap && brew install pinecone-io/tap/pinecone`
uv	Run the bundled Python scripts	Install guide

Getting started

Install the plugin from the Cursor Marketplace
Add PINECONE_API_KEY=your-key to a .env file at your workspace root (Cursor will load it into the MCP server via envFile)
Open Cursor Agent chat and type /pinecone-quickstart to get started
Verify the MCP server is connected: Cursor Settings > Features > Model Context Protocol

Verifying the installation

Component	Where to check
Skills	Cursor Settings > Rules — listed under "Agent Decides"
MCP Server	Cursor Settings > Features > Model Context Protocol
Commands	Type `/` in Agent chat and search

License

Apache-2.0