// Overview of all available Pinecone skills and what a user needs to get started. Invoke when a user asks what skills are available, how to get started with Pinecone, or what they need to set up before using any Pinecone skill.
Build n8n workflows using the Pinecone Assistant node or Pinecone Vector Store node. Use when building RAG pipelines, chat-with-docs workflows, configuring Pinecone nodes in n8n, troubleshooting Pinecone n8n nodes, or asking about best practices for Pinecone in n8n.
Create, manage, and chat with Pinecone Assistants for document Q&A with citations. Handles all assistant operations - create, upload, sync, chat, context retrieval, and list. Recognizes natural language like "create an assistant from my docs", "ask my assistant about X", or "upload my docs to Pinecone".
Guide for using the Pinecone CLI (pc) to manage Pinecone resources from the terminal. The CLI supports ALL index types (standard, integrated, sparse) and all vector operations — unlike the MCP which only supports integrated indexes. Use for batch operations, vector management, backups, namespaces, CI/CD automation, and full control over Pinecone resources.
Curated documentation reference for developers building with Pinecone. Contains links to official docs organized by topic and data format references. Use when writing Pinecone code, looking up API parameters, or needing the correct format for vectors or records.
Create, ingest into, and query a Pinecone full-text-search (FTS) index using the preview API (2026-01.alpha, public preview). Use when the user or agent asks to build a text search index on Pinecone, add dense or sparse vector fields, ingest documents, construct score_by clauses (text / query_string / dense_vector / sparse_vector), or compose with text-match filters ($match_phrase / $match_all / $match_any). Ships `scripts/ingest.py` for safe bulk ingestion (batch_upsert + error inspection + readiness polling); query construction is documented inline in this skill — write `documents.search(...)` calls directly, validated against `pc.preview.indexes.describe(...)` output.
Reference for the Pinecone MCP server tools. Documents all available tools - list-indexes, describe-index, describe-index-stats, create-index-for-model, upsert-records, search-records, cascading-search, and rerank-documents. Use when an agent needs to understand what Pinecone MCP tools are available, how to use them, or what parameters they accept.
| name | pinecone-help |
| description | Overview of all available Pinecone skills and what a user needs to get started. Invoke when a user asks what skills are available, how to get started with Pinecone, or what they need to set up before using any Pinecone skill. |
Pinecone is the leading vector database for building accurate and performant AI applications at scale in production. It's useful for building semantic search, retrieval augmented generation, recommendation systems, and agentic applications.
Here's everything you need to get started and a summary of all available skills.
Invoke any skill from Cursor Agent chat with /pinecone-<skill-name> (for example, /pinecone-quickstart or /pinecone-n8n).
.env file at your workspace root (the bundled MCP config loads it via Cursor's envFile field):
PINECONE_API_KEY=your-key
export PINECONE_API_KEY="your-key".| Tool | What it enables | Install |
|---|---|---|
| Pinecone MCP server | Use Pinecone directly inside your AI agent/IDE without writing code | Setup guide |
Pinecone CLI (pc) | Manage all index types from the terminal, batch operations, backups, CI/CD | brew tap pinecone-io/tap && brew install pinecone-io/tap/pinecone |
| uv | Run the packaged Python scripts included in these skills | Install uv |
| Skill | What it does |
|---|---|
pinecone-quickstart | Step-by-step onboarding — create an index, upload data, and run your first search |
pinecone-query | Search integrated indexes using natural language text via the Pinecone MCP |
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 |
pinecone-mcp | Reference for all Pinecone MCP server tools and their parameters |
pinecone-full-text-search | Build a full-text-search index — schema design, safe bulk ingestion, and query construction (text / query_string / dense / sparse scoring with text-match and metadata filters). Preview API (2026-01.alpha); requires pinecone Python SDK ≥ 9.0. |
pinecone-docs | Curated links to official Pinecone documentation, organized by topic |
pinecone-n8n | Build n8n workflows with the Pinecone Assistant node or Pinecone Vector Store node, including best practices and full workflow JSON generation |
Just getting started? → pinecone-quickstart
Want to search an index you already have?
pinecone-query (uses MCP)pinecone-cliWorking with documents and Q&A? → pinecone-assistant
Building a full-text search index (BM25-style keyword/phrase matching, optionally combined with dense or sparse vectors)? → pinecone-full-text-search (preview API, needs pinecone Python SDK ≥ 9.0)
Building an n8n workflow with Pinecone (RAG pipeline, chat with docs)? → pinecone-n8n
Need to manage indexes, bulk upload vectors, or automate workflows? → pinecone-cli
Looking up API parameters or SDK usage? → pinecone-docs
Need to understand what MCP tools are available? → pinecone-mcp