Pinecone Plugin for Claude Code

A lightweight plugin that integrates Pinecone vector database capabilities directly into Claude Code, enabling semantic search, index management, and RAG (Retrieval Augmented Generation) workflows.

Features

• Pinecone Assistant – Fully managed RAG service for document Q&A with citations, natural language support, and incremental file syncing
• Pinecone MCP Server – Full integration with the Pinecone Model Context Protocol server for index creation, listing, searching, and more
• Slash Commands – Quick access to common Pinecone operations directly from Claude Code
• Semantic Search – Query your vector indexes using natural language
• Natural Language Recognition – Assistant commands work without explicit slash commands

Installation

Option A: Claude Code Plugins Directory (Recommended)

Install from the official Claude Code Plugins Directory:

1. Install the plugin:

   /plugin install pinecone
   

2. Restart Claude Code to activate the plugin.

Option B: Pinecone Marketplace

Alternatively, install directly from the Pinecone marketplace:

1. Add the Pinecone plugin marketplace:

   /plugin marketplace add pinecone-io/pinecone-claude-code-plugin
   

2. Install the plugin:

   /plugin install pinecone@pinecone-claude-code-plugin
   

3. When prompted, select your preferred installation scope:

   • User scope (default) – Available across all your projects
   • Project scope – Shared with your team via version control
   • Local scope – Project-specific, not shared (gitignored)

4. Restart Claude Code to activate the plugin.

Set Your API Key

After installing via either method, configure your Pinecone API key before running Claude Code:

export PINECONE_API_KEY="your-api-key-here"

Don't have a Pinecone account? Sign up for free at app.pinecone.io

Install uv (Required for Assistant Commands)

To use Pinecone Assistant functionality, you must have uv installed. uv is a fast Python package and project manager:

macOS and Linux:

curl -LsSf https://astral.sh/uv/install.sh | sh

Windows:

powershell -ExecutionPolicy ByPass -c "irm https://astral.sh/uv/install.ps1 | iex"

With Homebrew:

brew install uv

After installation, restart your terminal and verify with: uv --version

Full installation guide: https://docs.astral.sh/uv/getting-started/installation/

Install the Pinecone CLI (Optional)

For additional command-line capabilities, install the Pinecone CLI:

brew tap pinecone-io/tap
brew install pinecone-io/tap/pinecone

Available Skills

/pinecone:help

Overview of all available Pinecone skills and what you need to get started. Run this when first installing the plugin.

/pinecone:quickstart

Interactive quickstart for new developers. Choose between two paths:

• Database — Create an integrated index, upsert data, and query using Pinecone MCP + Python
• Assistant — Create a Pinecone Assistant for document Q&A with citations

/pinecone:query

Query integrated indexes using natural language. Wraps the Pinecone MCP server for easy searching.

/pinecone:query query [your search text] index [indexName] namespace [ns] reranker [rerankModel]

Note: Only works with integrated indexes that use Pinecone's hosted embedding models.

/pinecone:full-text-search

End-to-end workflow for Pinecone's full-text-search (FTS) preview API (2026-01.alpha) — design a document schema, ingest a corpus, and construct documents.search(...) calls. Covers BM25 (text / query_string), dense_vector and sparse_vector scoring, and text-match filters ($match_phrase / $match_all / $match_any) for hybrid lexical+semantic queries.

Ships a scripts/ingest.py helper that does bulk batch_upsert with per-batch error inspection and post-upsert readiness polling — the three things bare-LLM ingest code reliably skips.

Requires pinecone Python SDK ≥ 9.0. The FTS document-schema API lives under pinecone.preview.

/pinecone:assistant

All-in-one skill for Pinecone Assistants — create, upload, sync, chat, context retrieval, and list. Works with both slash commands and natural language:

• "Create a Pinecone assistant from my docs"
• "Upload files from ./docs to my-assistant"
• "Sync my assistant with the docs folder"
• "Ask my assistant about authentication"
• "Search my assistant for context about embeddings"

Learn more: https://docs.pinecone.io/guides/assistant/quickstart

/pinecone:cli

Guide for using the Pinecone CLI (pc) to manage resources from the terminal. The CLI supports all index types and vector operations.

/pinecone:mcp

Reference for all Pinecone MCP server tools — parameters, usage, and examples.

/pinecone:docs

Curated documentation reference with links to official docs organized by topic and data format references.

MCP Server Tools

The plugin includes the full Pinecone MCP Server with the following tools:

For complete MCP server documentation, visit: Pinecone MCP Server Guide

Troubleshooting

"API Key not found" or access errors

Make sure your PINECONE_API_KEY environment variable is set correctly:

echo $PINECONE_API_KEY

If it's empty, set it and restart Claude Code.

MCP server not responding

1. Ensure you have Node.js installed (the MCP server runs via npx)
2. Check that your API key is valid
3. Restart Claude Code after setting environment variables

Query command not working with my index

The /query command only works with integrated indexes that use Pinecone's hosted embedding models. If you're using external embedding providers (OpenAI, HuggingFace, etc.), you'll need to use the MCP tools directly or wait for expanded support.

Assistant commands not working

Make sure you have uv installed. uv is required for all assistant commands:

# Verify uv is installed
uv --version

# Install if missing
curl -LsSf https://astral.sh/uv/install.sh | sh  # macOS/Linux

After installing uv, restart your terminal.

Keywords

pinecone · semantic search · vector search · vector database · retrieval · RAG · agentic RAG · sparse search · document Q&A · citations · assistant · managed RAG

License

MIT License – see LICENSE for details.

Have fun and enjoy developing with Pinecone! 🌲

{
  "name": "pinecone-plugins",
  "owner": {
    "name": "Pinecone"
  },
  "plugins": [
    {
      "name": "pinecone",
      "source": "./",
      "description": "Pinecone vector database integration. Streamline your Pinecone development with powerful tools for managing vector indexes, querying data, and rapid prototyping. Use slash commands like /quickstart to learn Pinecone and initialize Python projects and /query to quickly explore indexes. Access the Pinecone MCP server for creating, describing, upserting and querying indexes with Claude. Perfect for developers building semantic search, RAG applications, recommendation systems, and other vector-based applications with Pinecone."
    }
  ]
}