skills/pinecone-cli/SKILL.md
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.
npx skillsauth add pinecone-io/skills pinecone-cliInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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pc)Manage Pinecone from the terminal. The CLI is especially valuable for vector operations across all index types — something the MCP currently can't do.
| | CLI | MCP | |---|---|---| | Index types | All (standard, integrated, sparse) | Integrated only | | Vector ops (upsert, query, fetch, update, delete) | ✅ | ❌ | | Text search on integrated indexes | ✅ | ✅ | | Backups, namespaces, org/project mgmt | ✅ | ❌ | | CI/CD / scripting | ✅ | ❌ |
brew tap pinecone-io/tap
brew install pinecone-io/tap/pinecone
Other platforms (Linux, Windows) — download from GitHub Releases.
# Interactive (recommended for local dev)
pc login
pc target -o "my-org" -p "my-project"
# Service account (recommended for CI/CD)
pc auth configure --client-id "$PINECONE_CLIENT_ID" --client-secret "$PINECONE_CLIENT_SECRET"
# API key (quick testing)
pc config set-api-key $PINECONE_API_KEY
Check status: pc auth status · pc target --show
Note for agent sessions: If you need to run
pc logininside an agent loop, the browser auth link may not surface correctly. It's best to authenticate before starting an agent session. Runpc loginin your terminal directly, then invoke the agent once you're authenticated.
PINECONE_API_KEYpc login authenticates the CLI tool itself — it does not set PINECONE_API_KEY in your environment. Python scripts, Node.js SDKs, and other tools that use the Pinecone SDK need PINECONE_API_KEY set separately.
Use the CLI to create a key and export it in one step:
KEY=$(pc api-key create --name agent-sdk-key --json | jq -r '.value')
export PINECONE_API_KEY="$KEY"
Without jq: run pc api-key create --name agent-sdk-key --json and copy the "value" field manually.
| Task | Command |
|---|---|
| List indexes | pc index list |
| Create serverless index | pc index create -n my-index -d 1536 -m cosine -c aws -r us-east-1 |
| Index stats | pc index stats -n my-index |
| Upload vectors from file | pc index vector upsert -n my-index --file ./vectors.json |
| Query by vector | pc index vector query -n my-index --vector '[0.1, ...]' -k 10 --include-metadata |
| Query by vector ID | pc index vector query -n my-index --id "doc-123" -k 10 |
| Fetch vectors by ID | pc index vector fetch -n my-index --ids '["vec1","vec2"]' |
| List vector IDs | pc index vector list -n my-index |
| Delete vectors by filter | pc index vector delete -n my-index --filter '{"genre":"classical"}' |
| List namespaces | pc index namespace list -n my-index |
| Create backup | pc backup create -i my-index -n "my-backup" |
| JSON output (for scripting) | Add -j to any command |
Unlike the MCP, the CLI lets you query any index with raw vector values — useful when you generate embeddings externally (OpenAI, HuggingFace, etc.):
pc index vector query -n my-index \
--vector '[0.1, 0.2, ..., 0.9]' \
--filter '{"source":{"$eq":"docs"}}' \
-k 20 --include-metadata
jq -c '.embedding' doc.json | pc index vector query -n my-index --vector - -k 10
# Preview first
pc index vector update -n my-index \
--filter '{"env":{"$eq":"staging"}}' \
--metadata '{"env":"production"}' \
--dry-run
# Apply
pc index vector update -n my-index \
--filter '{"env":{"$eq":"staging"}}' \
--metadata '{"env":"production"}'
# Snapshot before a migration
pc backup create -i my-index -n "pre-migration"
# Restore to a new index if something goes wrong
pc backup restore -i <backup-uuid> -n my-index-restored
export PINECONE_CLIENT_ID="..."
export PINECONE_CLIENT_SECRET="..."
pc auth configure --client-id "$PINECONE_CLIENT_ID" --client-secret "$PINECONE_CLIENT_SECRET"
pc index vector upsert -n my-index --file ./vectors.jsonl --batch-size 1000
# Get all index names as a list
pc index list -j | jq -r '.[] | .name'
# Check if an index exists before creating
if ! pc index describe -n my-index -j 2>/dev/null | jq -e '.name' > /dev/null; then
pc index create -n my-index -d 1536 -m cosine -c aws -r us-east-1
fi
development
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.
data-ai
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.
tools
Interactive Pinecone quickstart for new developers. Choose between two paths - Database (create an integrated index, upsert data, and query using Pinecone MCP + Python) or Assistant (create a Pinecone Assistant for document Q&A). Use when a user wants to get started with Pinecone for the first time or wants a guided tour of Pinecone's tools.
tools
Query integrated indexes using text with Pinecone MCP. IMPORTANT - This skill ONLY works with integrated indexes (indexes with built-in Pinecone embedding models like multilingual-e5-large). For standard indexes or advanced vector operations, use the CLI skill instead. Requires PINECONE_API_KEY environment variable and Pinecone MCP server to be configured.