From pinecone
Provides overview of all Pinecone skills, required setup (account, API key), optional tools (CLI, MCP server, uv), and recommendations for which skill to use.
npx claudepluginhub pinecone-io/pinecone-claude-code-plugin --plugin pineconeThis skill is limited to using the following tools:
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.
Guides new Pinecone developers through interactive quickstart: Database path creates index, upserts data, queries via MCP/Python; Assistant path builds document Q&A tool. For first-time setup.
Implements vector databases with Pinecone, Weaviate, Qdrant, Milvus, pgvector for semantic search, RAG, recommendations, and similarity systems. Optimizes embeddings, indexing, and hybrid search.
Engineers vector DB solutions with Pinecone, Weaviate, Qdrant, Milvus, pgvector for RAG, semantic search, recommendations, and embedding optimization.
Share bugs, ideas, or general feedback.
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.
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:docs | Curated links to official Pinecone documentation, organized by topic |
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)
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