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 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

Installation

Step 1: Add the Pinecone Marketplace

Open Claude Code and add the Pinecone plugin marketplace:

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

Step 2: Install the Plugin

Install the Pinecone plugin interactively:

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

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)

After installation, restart Claude Code to activate the plugin.

Step 3: Set Your API Key

After installing the plugin, and before running Claude Code again you'll need to configure your Pinecone API key:

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

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

Optional: Install the Pinecone CLI

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

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

Available Commands

/pinecone:help

Display help information about the plugin, including:

• Available functionality
• API key configuration
• Troubleshooting tips

Run this when first installing the Plugin, then proceed to the quickstart.

/pinecone:quickstart

Get started quickly with Pinecone! This command:

1. Downloads and generates an AGENTS.md file, optimized for use with Claude Code and Pinecone
2. Walks you through a Python quickstart tutorial
3. Helps you create your first index and perform semantic searches

/pinecone:query

Query your Pinecone indexes using natural language. This command wraps the Pinecone MCP server for easy searching of integrated indexes. Most useful when you already have an integrated index created, and want to query it quickly from Claude.

Usage:

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

Parameters:

Parameter	Required	Description
query	Yes	The text to search for
index	Yes	The name of the Pinecone index to search
namespace	No	The namespace within the index
reranker	No	The reranking model to use for improved relevance

If you omit required arguments, the command will interactively guide you through selecting available indexes and namespaces.

Note: The /query command currently only works with integrated indexes that use Pinecone's hosted embedding models. Third-party embedding models (OpenAI, HuggingFace, etc.) are not yet supported.

MCP Server Tools

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

Tool	Description
list-indexes	List all available Pinecone indexes
describe-index	Get index configuration and namespaces
describe-index-stats	Get statistics including record counts and namespaces
search-records	Search records with optional metadata filtering and reranking
create-index-for-model	Create a new index with integrated embeddings
upsert-records	Insert or update records in an index
rerank-documents	Rerank documents using a specified reranking model

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.

Keywords

pinecone · semantic search · vector search · vector database · retrieval · RAG · agentic RAG · sparse search

License

MIT License – see LICENSE for details.

Have fun and enjoy developing with Pinecone! 🌲