pinecone-help | SkillBundle

Pinecone Skills — Help & Overview

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.

What You Need

Required

Pinecone account — free to create at https://app.pinecone.io/?sessionType=signup
API key — create one in the Pinecone console after signing up, then either export it in your terminal:
```
export PINECONE_API_KEY="your-key"
```
Or add it to a .env file if your IDE doesn't inherit shell variables: PINECONE_API_KEY=your-key

Optional (unlock more capabilities)

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

Available Skills

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

Which skill should I use?

Just getting started? → pinecone-quickstart

Want to search an index you already have?

Integrated index (built-in embedding model) → pinecone-query (uses MCP)
Any other index type → pinecone-cli

Working 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

Pinecone Skills — Help & Overview

Here's everything you need to get started and a summary of all available skills.

What You Need

Required

Pinecone account — free to create at https://app.pinecone.io/?sessionType=signup
API key — create one in the Pinecone console after signing up, then either export it in your terminal:
```
export PINECONE_API_KEY="your-key"
```
Or add it to a .env file if your IDE doesn't inherit shell variables: PINECONE_API_KEY=your-key

Optional (unlock more capabilities)

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

Available Skills

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

Which skill should I use?

Just getting started? → pinecone-quickstart

Want to search an index you already have?

Integrated index (built-in embedding model) → pinecone-query (uses MCP)
Any other index type → pinecone-cli

Working with documents and Q&A? → pinecone-assistant

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