Official Pinecone plugin for Cursor. Build semantic search, RAG, recommendation systems, and other vector-based applications with Pinecone — directly from your editor.
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. |
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
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 |
Run the following command in Cursor chat:
/add-plugin pinecone
Or install directly from the marketplace: cursor.com/marketplace/pinecone
- Pinecone account — free at app.pinecone.io
- API key — create one in the Pinecone console, then add it to a
.envfile at your workspace root:The bundled MCP config loads this file via Cursor'sPINECONE_API_KEY=your-keyenvFilefield, 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)
| 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 |
- Install the plugin from the Cursor Marketplace
- Add
PINECONE_API_KEY=your-keyto a.envfile at your workspace root (Cursor will load it into the MCP server viaenvFile) - Open Cursor Agent chat and type
/pinecone-quickstartto get started - Verify the MCP server is connected: Cursor Settings > Features > Model Context Protocol
| 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 |